CN112800673A - Method for predicting blasting block degree based on SA-GA-BP algorithm - Google Patents
Method for predicting blasting block degree based on SA-GA-BP algorithm Download PDFInfo
- Publication number
- CN112800673A CN112800673A CN202110107508.1A CN202110107508A CN112800673A CN 112800673 A CN112800673 A CN 112800673A CN 202110107508 A CN202110107508 A CN 202110107508A CN 112800673 A CN112800673 A CN 112800673A
- Authority
- CN
- China
- Prior art keywords
- blasting
- neural network
- population
- algorithm
- prediction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005422 blasting Methods 0.000 title claims abstract description 92
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000013528 artificial neural network Methods 0.000 claims abstract description 81
- 238000002922 simulated annealing Methods 0.000 claims abstract description 45
- 230000002068 genetic effect Effects 0.000 claims abstract description 43
- 238000012549 training Methods 0.000 claims abstract description 39
- 239000011435 rock Substances 0.000 claims abstract description 22
- 238000005553 drilling Methods 0.000 claims abstract description 15
- 239000000843 powder Substances 0.000 claims abstract description 11
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000005457 optimization Methods 0.000 claims abstract description 6
- 230000035772 mutation Effects 0.000 claims description 13
- 238000010606 normalization Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 8
- 238000005070 sampling Methods 0.000 claims description 8
- 206010064571 Gene mutation Diseases 0.000 claims description 7
- 238000001816 cooling Methods 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 6
- 239000003814 drug Substances 0.000 claims description 5
- 230000006870 function Effects 0.000 description 27
- 108090000623 proteins and genes Proteins 0.000 description 14
- 230000008569 process Effects 0.000 description 10
- 230000003044 adaptive effect Effects 0.000 description 9
- 239000002360 explosive Substances 0.000 description 5
- 238000010276 construction Methods 0.000 description 3
- 238000012886 linear function Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 238000004880 explosion Methods 0.000 description 2
- 238000011065 in-situ storage Methods 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Physiology (AREA)
- Genetics & Genomics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method for predicting blasting block size based on an SA-GA-BP algorithm, and belongs to the technical field of blasting block size prediction. The method comprises the steps of obtaining blasting lumpiness influence factors in a blasting engineering site, and then determining a step height-to-drilling load ratio H/B, a spacing-to-load ratio S/B, a load-to-aperture ratio B/D, a stemming-to-load ratio T/B and a powder factor P according to a primary analytic methodfRock modulus of elasticity E and field bulk size XBAs training samples and prediction samples; determining BP neural network topological structure, calculating optimal weight and threshold by using genetic simulated annealing algorithm (SA-GA), anddecoding and assigning the decoding to a BP neural network system for training; initially constructing a blasting blockiness prediction model; carrying out error analysis on the prediction result; and finally, performing field prediction on the blasting block size in the blasting engineering field. According to the method, the optimal solution can be searched only by a small amount of samples through the optimization of the improved hybrid intelligent algorithm, the convergence speed is improved, and the situation of falling into the local optimal solution is avoided.
Description
Technical Field
The invention relates to a method for predicting blasting block size based on an SA-GA-BP algorithm, belonging to the technical field of blasting block size prediction.
Background
Rock blasting technology is more and more widely applied to water conservancy, mine, traffic and other departments. The rock broken by blasting is mostly rock body with cracks, joints and the like which are cut by a plurality of different weak structural planes. In order to reveal the mechanism of explosive fracture of jointed rock, various methods have been used to study it. The rock blasting block size distribution has important theoretical and practical significance, and on one hand, the rock blasting block size and the distribution rule thereof are important bases for judging the blasting effect; on the other hand, the analysis of the rock blasting lump size distribution is an important way for further researching the blasting breaking mechanism and optimizing the blasting parameters.
Scholars at home and abroad put forward a plurality of prediction theoretical models of blasting blockiness, and the classical model has a Kutzniuzov semi-empirical formula, an kuz-Ram model, a Rosin-Rammler model and the like. However, blasting is a complex nonlinear process, and the classical theoretical models are established on the basis of certain assumed conditions, only some major factors are considered, and some minor factors are ignored, so that the models have certain limitations. With the rapid development of artificial intelligence technology in recent years, the application of machine learning methods in various engineering fields is favored by researchers, such as BP neural networks, support vector machines, GEPs, and the like. At present, the BP neural network is widely applied to the aspect of blasting block size prediction.
Although the application of the BP neural network prediction blasting block size is wide, some problems exist. The traditional BP neural network has the defects of low learning speed, easy falling into local extremum and the like. In the process of practical application, the learning efficiency is low, the prediction precision is not high, and the reliability is not strong.
Disclosure of Invention
The invention provides a method for predicting blasting blockiness based on an SA-GA-BP algorithm, aiming at the problems of low learning efficiency, low prediction precision and low reliability of the conventional BP neural network prediction blasting blockiness.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a method for predicting blasting block size based on SA-GA-BP algorithm comprises the following steps:
(1) obtaining blasting block size influence factors in a blasting engineering site, and determining a step height-to-drilling load ratio H/B, a spacing-to-load ratio S/B, a load-to-aperture ratio B/D, a stemming-to-load ratio T/B and a powder factor P according to a primary analytic hierarchy processfRock modulus of elasticity E and field bulk size XBAs input parameter, the blasting blockiness X50Constructing a data set for the output parameters as a training sample and a prediction sample, and carrying out normalization processing on original data of the data set constructed by the training sample and the prediction sample; wherein the blasting blockiness influence factors comprise hole pitch, row pitch, aperture, hole depth, stuffing, chassis resistance lines, total explosive loading, maximum segment explosive loading, unit consumption, height difference, blasting source pitch and rock physical and mechanical properties; the raw data comprises input parameters and output parameters;
(2) determining the number of input layers, output layers and hidden layers according to the input parameters and the output parameters respectively to determine a neural network topological structure, and establishing a BP neural network initial model;
(3) optimizing the BP neural network by the genetic simulated annealing algorithm (comprising the steps (3) to (5)): calculating a BP neural network connection weight and a threshold by using a genetic algorithm, decoding and assigning the weight to a BP neural network system, and training the training sample in the step (1) by using the BP neural network system to obtain a new generation of population; in the genetic algorithm, each sample of a training sample is used as a random initial population, coding is carried out, and genetic operations of selection, crossing and variation are carried out to generate a new generation of population;
(4) judging whether the new generation population in the step (3) tends to a stable stage, and if so, optimizing a BP neural network by adopting a simulated annealing algorithm to obtain an optimal solution; if not, returning to the step (3) to train the BP neural network prediction model by taking the new generation population as a training sample, and optimizing the BP neural network connection weight and the threshold by using a Genetic Algorithm (GA);
(5) giving the optimal solution which tends to be stable in the step (4) to a BP neural network system to update the weight and the threshold value, and initially constructing a blasting block degree prediction model;
(6) inputting the prediction sample obtained in the step (1) into the blasting block degree prediction model constructed in the step (5), and carrying out error analysis on an output result; if the error exceeds the set threshold, returning to the step (3) to adjust the genetic iteration times of the Genetic Algorithm (GA) to continuously optimize the obtained connection weight and the threshold, and if the error does not exceed the set threshold, completing the blasting block degree prediction model of the engineering field;
further, the threshold value initially set in the step (6) is not more than 10%;
(7) and (4) predicting the blasting block degree of the engineering site by using the blasting block degree prediction model finished in the step (6).
The blasting lump size influence factor of the step (1) comprises the following steps: step height H, drilling load B, spacing S, row spacing B, aperture D and hole depth H1And stuffing h2Chassis resistance line w, total medicine loading quantity Q, unit consumption Q and bulletModulus of elasticity E, size of field bulk XBAnd petrophysical-mechanical properties;
the step (2) of inputting parameters comprises the following steps: step height to borehole load ratio (H/B), spacing to load ratio (S/B), load to aperture ratio (B/D), stemming to load ratio (T/B), powder factor (P)f) Rock modulus of elasticity (E) and in situ bulk size (X)B) The output parameters include: blasting bulk density (X)50)；
The preliminary construction of the BP neural network model optimized by the genetic simulated annealing algorithm comprises the following specific steps:
1) the step height to drilling load ratio (H/B), the spacing to load ratio (S/B), the load to aperture ratio (B/D), the stemming to load ratio (T/B) and the powder factor (P) of the new generation of populationf) Rock modulus of elasticity (E) and in situ bulk size (X)B) The data form a seven-dimensional vector, an initial population is randomly generated according to the individual vector and the individual is coded, and as the simulated annealing algorithm is mainly used for optimizing the connection weight and the threshold of a neural network system, a fitness function is constructed according to network errors;
2) calculating the fitness value of the population according to the fitness function relation, and arranging the individuals of the new generation of population according to the sequence of the fitness values from small to large; the smaller the fitness value, the better the individual gene;
3) averagely dividing the new generation population into three grades according to the size of the fitness value, namely dividing the new generation population into three grades of good grade, medium grade and poor grade, wherein the smaller the fitness value is, the better the grade is; randomly sampling and selecting individuals in three grades of good, medium and poor according to the ratio of 5:3:2 of good, medium and poor respectively to form a new population A;
4) carrying out self-adaptive cross and variation treatment on the population A: with probability P1Recombining the individuals to generate a new individual a so as to ensure good genes of the offspring; probability of re-passing P2Carrying out mutation processing on the new individual a to obtain a new individual b so as to continue the diversity of the population, and carrying out adaptive probability P1Eliminating gene mutation individuals so as to prevent the excellent individuals from generating gene mutation to influence offspring individuals to obtain a population B;
5) judging the stability of the population B, and if the population is stable, entering the optimization operation of the step 6); if the population is unstable, returning to the step 3) to re-sample and select individuals and execute genetic operation;
6) taking the optimal individual in the stable population as the initial solution S of a Simulated Annealing (SA) algorithm0And generating a new solution S using the state function1；
7) Taking the fitness function as a target evaluation function of the simulated annealing operation, and carrying out S) on the initial solution of the step 6)0And new solution S1Evaluating, selecting whether to accept a new solution according to a Metropolis principle, and if so, outputting an optimal solution; if not, cooling according to the temperature decay function, returning to the step 6) to regenerate the new solution S1；
8) Decoding the optimal solution obtained in the step 7), and updating the BP neural network connection weight and the threshold to obtain an optimal weight threshold prediction model.
The genetic algorithm mainly improves the aspects of coding mode, operator selection, crossover operator, mutation operator and the like, and the simulated annealing algorithm mainly improves the aspects of initial temperature selection, temperature reduction attenuation function, algorithm termination selection, target error function introduction and the like; a BP neural network connection weight and a threshold are optimized by improving a genetic simulated annealing algorithm, so that the BP neural network connection weight and the threshold are infinitely approximated to a target function to be obtained, and the purpose of reducing system errors is achieved.
The invention has the beneficial effects that:
(1) the invention provides a blasting blockiness prediction method for optimizing a BP (back propagation) neural network based on an improved genetic simulated annealing algorithm, which is characterized in that on the basis of the BP neural network system, the improved genetic simulated annealing algorithm is utilized to optimize a connection weight and a threshold value in the BP neural network system, so that the connection weight and the threshold value are infinitely approximated to a highly nonlinear mapping relation between an input factor and an output factor;
(2) the invention provides a blasting block size prediction method for optimizing a BP neural network based on an improved genetic simulated annealing algorithm, which is characterized in that aiming at each factor in a blasting engineering field, a plurality of blasting parameters influencing the vibration speed are selected as influencing factors by a main analytic hierarchy process, and a blasting block size prediction model aiming at a specific engineering is constructed by combining blasting data samples of the engineering;
(3) based on the nonlinear characteristic of the BP neural network, the method can be well suitable for blasting block degree prediction with a very complex action mechanism, the prediction precision is higher than that of a Sagnac formula and various improved formulas thereof, and simulation is closer to the real situation of a field;
(4) the prediction method of the improved hybrid intelligent algorithm overcomes the difficulties that the number of initial samples required by the traditional neural network prediction is more and the training precision is high, can search the optimal solution only by optimizing a small number of samples through the improved hybrid intelligent algorithm, improves the convergence speed and avoids the situation of trapping in the local optimal solution.
Drawings
FIG. 1 is a flow chart of a method for predicting blasting blockiness based on an SA-GA-BP algorithm;
FIG. 2 is a process for optimizing BP neural network parameters by improving a genetic simulated annealing algorithm.
Detailed Description
The present invention will be further described with reference to the following embodiments.
Example 1: as shown in fig. 1, a method for predicting blasting block size based on SA-GA-BP algorithm includes the following steps:
(1) acquiring blasting block size influence factors in a blasting engineering field: step height H, drilling load B, spacing S, row spacing B, aperture D and hole depth H1And stuffing h2A chassis resistance line w, a total medicine loading quantity Q, unit consumption Q, an elastic modulus E and a field block size XBAnd the physical and mechanical properties of rock, determining the step height-to-drilling load ratio H/B, the spacing-to-loading ratio S/B, the loading-to-aperture ratio B/D, the stemming-to-loading ratio T/B and the powder factor P according to the primary analytic hierarchy processfRock modulus of elasticity E and field bulk size XBAs input parameter, the blasting blockiness X50Constructing a data set for the output parameters as a training sample and a prediction sample; because the physical dimension is different, the original data of the data set constructed by the training sample and the prediction sample are unified to be normalized; wherein the raw data comprises input parameters and output parameters;
(2) determining the number of input layers, output layers and hidden layers according to the input parameters and the output parameters respectively to determine a neural network topological structure, and establishing a BP neural network initial model;
(3) optimizing the BP neural network by the genetic simulated annealing algorithm (comprising the steps (3) to (5)): training the BP neural network prediction model by using a training sample, calculating a BP neural network connection weight and a threshold by using a Genetic Algorithm (GA), decoding and assigning to a BP neural network system, and training the training sample in the step (1) by using the BP neural network system to obtain a new generation population; in the genetic algorithm, each sample of a training sample is used as a random initial population, coding is carried out, and genetic operations of selection, crossing and variation are carried out to generate a new generation of population;
(4) judging whether the new generation population in the step (3) tends to a stable stage, and if so, optimizing and optimizing a BP neural network by adopting a simulated annealing algorithm to obtain an optimal solution; if not, returning to the step (3) to train the BP neural network prediction model by taking the new generation population as a training sample, and optimizing the BP neural network connection weight and the threshold by using a Genetic Algorithm (GA);
(5) giving the optimal solution which tends to be stable and optimized in the step (4) to a BP neural network system to update the weight and the threshold value, and initially constructing a blasting blockiness prediction model;
the optimization of BP neural network by the improved genetic simulated annealing algorithm comprises the following specific steps (see figure 2)
1) Forming a seven-dimensional vector by using data of hole depth, filling, chassis resistance line, height difference, explosion source distance, total medicine loading quantity and maximum medicine loading quantity of a new generation of population, randomly generating an initial population according to an individual vector and coding individuals of the initial population, and constructing a fitness function according to network errors as a simulated annealing algorithm is mainly used for optimizing a neural network system connection weight and a threshold, wherein the fitness function is as shown in a formula (1);
where y (k) represents the k sample actual output value and y' (k) represents the k sample net output value;
2) calculating the fitness value of the population according to the fitness function relation (1), and arranging the individuals of the new generation of population according to the sequence of the fitness values from small to large; the smaller the fitness value is, the better the individual gene is, so the individual sequence of the new population is arranged from good to bad;
3) evenly dividing the new generation of population into 3 levels of good and medium differences according to the size of the fitness value, randomly sampling and selecting individuals in each level, wherein the random sampling quantity is 5:3:2 according to the ratio of good to medium differences to form a new population A;
4) carrying out self-adaptive cross and variation treatment on the population A: with probability P1Recombining the individuals to generate a new individual A so as to ensure good genes of the offspring;
wherein, P10And P11For greater and lesser initial crossover probabilities, fmin、fmaxF is the minimum adaptive value of the current population, the average adaptive value and the smaller adaptive value in the crossed individuals;
the method for pairwise cross variation in the population comprises
Wherein x1，x2For individuals who are cross-forward selected, x'1，x′2Is the individual generated after crossing, c is [0, 1 ]]A random number of (c);
adaptive mutation operation probability P2Determining a gene to be mutated of an individual by adopting a random sampling method when the individual is selected, and then carrying out mutation on the gene by adopting a non-uniform mutation operator in the following mutation mode;
wherein: x' is a gene xiGenes, G, produced after mutationmaxThe total iteration times in the current evolutionary algebra and algorithm; c. C1Is a random number on (0,1), ai，biThe maximum value and the minimum value in the value range of the variation gene are obtained;
probability of re-passing P2Carrying out mutation processing on the new individual A to obtain a new individual B so as to extend the diversity of the population through the self-adaptive probability P1Eliminating gene mutation individuals so as to prevent the excellent individuals from generating gene mutation to influence offspring individuals to obtain a population B;
5) judging the stability of the population B, and if the population is stable, entering the simulated annealing operation of the step 6); if the population is unstable, returning to the step 3) to re-sample and select individuals and execute genetic operation;
the stability determination method comprises
Wherein: f. ofmaxIs the mean fitness in the population, fminFor optimal individual fitness values in the population, when the average individual fitness value is approximately equal to the optimal fitness value (i.e., equation (5) is satisfied), wherein
A preset value), at which time, population evolution tends to a stable stage;
6) taking the optimal individual in the stable population as the initial solution S of a Simulated Annealing (SA) algorithm0And generating a new solution S using the state function1；
The state function is as follows:
S1＝S0+α/20 (6)
wherein alpha is a random number distributed uniformly in [ -0.5, 0.5]
7) Adopting the fitness function F to S in the step 1)0And S1Starting to evaluate, and judging new solution S according to Metropolis principle1Whether accepted or not, the acceptance probability Q is defined as follows:
T0is the initial set temperature;
8) judging whether a Simulated Annealing (SA) algorithm end condition is met, and if so, outputting an optimal solution; if not, cooling according to the temperature attenuation function, and then turning to the step 6) to continue to execute the simulated annealing operation until the SA termination condition is met;
temperature decay function selection linear function
Tk+1＝kt×Tk (8)
Wherein k istTaking 0.9 as the cooling rate;
9) decoding the optimal solution obtained in the step 8), and updating the BP neural network connection weight and the threshold to obtain an optimal weight threshold prediction model;
(6) inputting the prediction sample obtained in the step (1) into the optimal weight threshold prediction model (blasting block degree prediction model) obtained in the step (5), and carrying out error analysis on an output result; if the error exceeds the initially set threshold (the initially set threshold is not more than 10%), returning to the step (3), adjusting the iteration times of the Genetic Algorithm (GA), and if the error does not exceed the initially set threshold, completing the construction process of the blasting block degree prediction model of the engineering site, namely the optimal weight threshold prediction model of the engineering site;
(7) and (4) predicting the blasting block degree of the engineering site by using the blasting block degree prediction model finished in the step (6).
Example 2: the embodiment further describes the technical solution provided by the present invention with reference to specific examples;
in the example, a certain surface mine blasting project is taken as a research object, the mine blasting data is screened and collected, characteristics and indexes capable of embodying the surface mine blasting process are determined, and a learning sample based on an SA-GA-BP model is constructed and comprises output parameters and input parameters;
the main factors influencing the rock blasting lumpiness are blasting parameters, explosive parameters and rock mechanical properties. The blasting parameters comprise factors such as step height (H), drilling load (B), spacing (S), aperture (D), stemming (T) and the like; in the embodiment, a step height-to-drilling load ratio (H/B), a spacing-to-load ratio (S/B), a load-to-aperture ratio (B/D), a stemming-to-load ratio (T/B) are taken as blasting parameters, a powder factor (Pf) is taken as a parameter reflecting the property of explosive, and an SA-GA-BP network rock blasting blockiness prediction model is established based on 7 parameters such as blasting parameters, elastic modulus, field blockiness and the like;
a method for predicting blasting blockiness based on an SA-GA-BP algorithm (see figure 1) comprises the following specific steps:
(1) acquiring blasting block size influence factors in a blasting engineering field: step height (H), drilling load (B), spacing (S), row spacing (B), aperture (D), hole depth (H1), filling (H2), chassis resistance line (w), total loading (Q), unit consumption (Q), elastic modulus (E), field block size (X)B) Determining a step height and drilling load ratio (H/B), a spacing and load ratio (S/B), a load and aperture ratio (B/D), a stemming and load ratio (T/B), a powder factor (Pf), a rock elastic modulus (E) and a field block size (XB) according to a primary analytic hierarchy process as input parameters, and constructing a data set as a training sample and a prediction sample by taking the prediction block size (X50) as an output parameter; because the physical dimension is different, the original data of the data set constructed by the training sample and the prediction sample are unified to be normalized; wherein the raw data comprises input parameters and output parameters;
in the embodiment, before the training operation of the neural network prediction model of the genetic simulated annealing algorithm is executed, in order to prevent the dimension of different input parameters from generating errors on the result and reduce the influence of the errors of the maximum value and the minimum value of the data on the whole training sample, the data is preprocessed by normalization;
carrying out normalization pretreatment on the data, wherein the normalization process formula is as follows:
in the formula: x is the data set before preprocessing, Xmax，XminThe maximum value and the minimum value of X are obtained, and Y is a normalized data set; in the example, x and y can be expressed as any data set of a step height-to-drilling load ratio (H/B), a spacing-to-load ratio (S/B), a load-to-aperture ratio (B/D), a stemming-to-load ratio (T/B), a powder factor (Pf), a rock elastic modulus (E) and a field lumpiness size (XB);
the preprocessing program can be realized by MATLAB, wherein normalization can be converted by a MATLAB linear function; carrying out inverse normalization processing on the output data, wherein the inverse normalization processing is realized by adopting a command POSIMNMX;
table 1 shows 40 groups of test set data, the first 35 groups of data constitute training samples, and the last 10 groups of data constitute prediction samples;
TABLE 1 blasting engineering 40 groups of blasting lump size monitoring data of a certain surface mine
In the embodiment, a matlab neural network tool box is used for prediction, and when training samples are completely trained and controlled within error precision, the optimal weight threshold model is established;
(2) determining the number of input layers, output layers and hidden layers according to the input parameters and the output parameters respectively to determine a neural network topological structure, and establishing a BP neural network initial model;
because the input parameters adopt 7 indexes and the output parameters adopt 2 indexes, the input layer and the output layer of the BP neural network respectively adopt 7 layers and 1 layer, but the hidden layer is not determined by a proper formula at present, the number of the hidden layers is obtained according to an empirical formula in the common method, in order to reduce the iteration times and the operation time cost of the whole model in the operation process and increase the prediction precision, the hidden layer is optimized by a Genetic Algorithm (GA), the prediction precision is highest when the hidden layer is 13, and therefore 13 is selected as the number of the hidden layers, the topological structure of the BP neural network is determined to be 7-13-1;
selecting a Sigmoid function for the excitation function of the hidden layer node, and selecting a linear transmission function for the excitation function of the output layer node; meanwhile, the maximum training frequency is set to be 1000, the relative MSE target value is 0.001, and the learning rate is 0.1;
in the Genetic Algorithm (GA), because data are preprocessed, the value range of an initial population is set to be (-3,3), the population scale is 100, the maximum algebra is 200, the cross probability is 0.5, and the variation probability is 0.2;
(3) optimizing the BP neural network by the genetic simulated annealing algorithm (comprising the steps (3) to (5)): training the BP neural network prediction model by using a training sample, calculating a BP neural network connection weight and a threshold by using a Genetic Algorithm (GA), decoding and assigning to a BP neural network system, and training the training sample in the step (1) by using the BP neural network system to obtain a new generation population; in the genetic algorithm, each sample of a training sample is used as a random initial population, coding is carried out, and genetic operations of selection, crossing and variation are carried out to generate a new generation of population;
(4) judging whether the new generation population in the step (3) tends to a stable stage, and if so, optimizing and optimizing a BP neural network by adopting a simulated annealing algorithm to obtain an optimal solution; if not, returning to the step (3) to train the BP neural network prediction model by taking the new generation population as a training sample, and optimizing the BP neural network connection weight and the threshold by using a Genetic Algorithm (GA);
(5) giving the optimal solution which tends to be stable and optimized in the step (4) to a BP neural network system to update the weight and the threshold value, and initially constructing a blasting blockiness prediction model;
the optimization of the BP neural network by the improved genetic simulated annealing algorithm comprises the following specific steps (see figure 2),
1) forming a seven-dimensional vector by using data of step height, drilling load ratio (H/B), spacing and load ratio (S/B), load and aperture ratio (B/D), stemming and load ratio (T/B), powder factor (Pf), rock elastic modulus (E) and field block size (XB) of a new generation of population, randomly generating an initial population according to the individual vector and coding the individual, and constructing a fitness function according to network errors because a simulated annealing algorithm is mainly used for optimizing the connection weight and threshold of a neural network system, wherein the fitness function is as shown in formula (1);
where y (k) represents the k sample actual output value and y' (k) represents the k sample net output value;
2) calculating the fitness value of the population according to the fitness function relation (1), and arranging the individuals of the new generation of population according to the sequence of the fitness values from small to large; the smaller the fitness value is, the better the individual gene is, so the individual sequence of the new population is arranged from good to bad;
3) evenly dividing the new generation of population into 3 levels of good and medium differences according to the size of the fitness value, randomly sampling and selecting individuals in each level, wherein the random sampling quantity is 5:3:2 according to the ratio of good to medium differences to form a new population A;
4) carrying out self-adaptive cross and variation treatment on the population A: with probability P1Recombining the individuals to generate a new individual A so as to ensure good genes of the offspring;
wherein, P10And P11For greater and lesser initial crossover probabilities, fmin、fmaxF is the minimum adaptive value of the current population, the average adaptive value and the smaller adaptive value in the crossed individuals;
the method for pairwise cross variation in the population comprises
Wherein x1，x2For individuals who are cross-forward selected, x'1，x′2Is the individual generated after crossing, c is [0, 1 ]]A random number of (c);
adaptive mutation operation probability P2Determining a gene to be mutated of an individual by adopting a random sampling method when the individual is selected, and then carrying out mutation on the gene by adopting a non-uniform mutation operator in the following mutation mode;
wherein: x' is a gene xiGenes, G, produced after mutationmaxThe total iteration times in the current evolutionary algebra and algorithm; c. C1Is a random number on (0,1), ai，biThe maximum value and the minimum value in the value range of the variation gene are obtained;
probability of re-passing P2Carrying out mutation processing on the new individual A to obtain a new individual B so as to extend the diversity of the population through the self-adaptive probability P1Eliminating gene mutation individuals so as to prevent the excellent individuals from generating gene mutation to influence offspring individuals to obtain a population B;
5) judging the stability of the population B, and if the population is stable, entering the simulated annealing operation of the step 6); if the population is unstable, returning to the step 3) to re-sample and select individuals and execute genetic operation;
the stability determination method comprises
Wherein: f. ofmaxIs the mean fitness in the population, fminFor optimal individual fitness values in the population, when the average individual fitness value is approximately equal to the optimal fitness value (i.e., equation (5) is satisfied), wherein
A preset value) at which the population evolves toward a stable orderA segment;
6) taking the optimal individual in the stable population as the initial solution S of a Simulated Annealing (SA) algorithm0And generating a new solution S using the state function1；
The state function is as follows:
S1＝S0+α/20 (6)
wherein alpha is a random number distributed uniformly in [ -0.5, 0.5]
7) Adopting the fitness function F to S in the step 1)0And S1Starting to evaluate, and judging new solution S according to Metropolis principle1Whether accepted or not, the acceptance probability Q is defined as follows:
T0is the initial set temperature;
8) judging whether a Simulated Annealing (SA) algorithm end condition is met, and if so, outputting an optimal solution; if not, cooling according to the temperature attenuation function, and then turning to the step 6) to continue to execute the simulated annealing operation until the SA termination condition is met;
temperature decay function selection linear function
Tk+1＝kt×Tk (8)
Wherein k istTaking 0.9 as the cooling rate;
9) decoding the optimal solution obtained in the step 8), and updating the BP neural network connection weight and the threshold to obtain an optimal weight threshold prediction model;
(6) inputting the prediction sample obtained in the step (1) into the optimal weight threshold prediction model (blasting block degree prediction model) obtained in the step (5), and carrying out error analysis on an output result; if the error exceeds the initially set threshold (the initially set threshold is not more than 10%), returning to the step (3), adjusting the iteration times of the Genetic Algorithm (GA), and if the error does not exceed the initially set threshold, completing the construction process of the blasting block degree prediction model of the engineering site, namely the optimal weight threshold prediction model of the engineering site;
in order to evaluate the accuracy of the prediction precision of the GA-SA-BP model, the GA-BP model is constructed by using the same training sample and the parameters of the prediction model to predict the prediction sample, the result is compared with the GA-SA-BP model, the relative error is used as the evaluation standard,
the relative error is taken as the evaluation standard, and the predicted values and the relative errors of the least square method, the GA-BP model and the SA-GA-BP model are shown in a table 2;
TABLE 2 predicted values and relative errors of the least squares method, GA-BP model and SA-GA-BP model
Evaluation indexes of the GA-BP model and the GA-SA-BP model are shown in Table 3
TABLE 3 evaluation indexes of GA-BP model and GA-SA-BP model
| Prediction model | R2 | RMSE | MAE |
| SA-GA-BP-15 | 0.9804 | 0.0012 | 0.1781 |
| SA-GA-BP-35 | 0.9990 | 0.0001 | 0.0345 |
| GA-BP-15 | 0.9247 | 0.0072 | 0.3933 |
| GA-BP-35 | 0.9831 | 0.0009416 | 0.1195 |
As can be seen from Table 2, the average relative errors of the blasting blockiness predicted by the least square method and the GA-BP neural network are respectively 16.15% and 25.92%, while the average relative error predicted by the SA-GA-BP neural network is only 9.66%, which shows the superiority of the prediction performance; from table 2, it can be known that the error of the least square method is the largest, and the reason for this is that the least square method has a large error because the least square method performs linear fitting on the influence factors and the blasting particle size, and the relationship between the blasting influence factors and the particle size may be nonlinear; the GA-BP neural network has better learning and mapping capabilities, simultaneously has a self-learning function, and can continuously train the network with new data and correct the prediction model, so that the GA-BP neural network has an accurate prediction result compared with a least square method, and the SA-GA-BP neural network removes the correlation among parameters by using a principal component analysis method, thereby improving the prediction precision of the model and ensuring that the SA-GA-BP neural network has an accurate prediction result compared with the GA-BP neural network; the SA-GA-BP prediction model can effectively and accurately predict the explosion blocking degree;
(7) and (4) predicting the blasting block degree of the engineering site by using the blasting block degree prediction model finished in the step (6).
The invention optimizes BP neural network parameters based on a genetic simulated annealing algorithm; and comprehensively considering the engineering site, and combining the blasting data sample of the engineering site to construct a blasting block size prediction model of the specific engineering. The invention solves the problem that the traditional algorithm needs a large number of training samples during prediction, can improve the prediction precision by only a small number of samples, has high convergence speed and can effectively avoid the problem of falling into the local optimal solution. The method is simple and convenient to operate and strong in overall search capability; and the prediction result can be expressed visually, and is simple and easy to understand.
Although the present invention has been described in detail with reference to the specific embodiments, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art. It should be understood that any modification, replacement, or improvement made within the spirit and scope of the present invention should be included in the following claims.
Claims (4)
1. A method for predicting blasting block size based on SA-GA-BP algorithm is characterized by comprising the following specific steps:
(1) obtaining blasting block size influence factors in a blasting engineering site, and determining a step height-to-drilling load ratio H/B, a spacing-to-load ratio S/B, a load-to-aperture ratio B/D, a stemming-to-load ratio T/B and a powder factor P according to a primary analytic hierarchy processfRock modulus of elasticity E and field bulk size XBAs input parameter, the blasting blockiness X50Constructing a data set for the output parameters as a training sample and a prediction sample, and carrying out normalization processing on original data of the data set constructed by the training sample and the prediction sample; wherein the raw data comprises input parameters and output parameters;
(2) respectively determining the number of an input layer, an output layer and a hidden layer according to the input parameters and the output parameters, determining a topological structure of the BP neural network according to the number of the input layer, the output layer and the hidden layer, and establishing an initial model of the BP neural network;
(3) optimizing a BP neural network by a genetic simulated annealing algorithm: training the BP neural network prediction model by using a training sample, calculating a BP neural network connection weight and a threshold by using a Genetic Algorithm (GA), decoding and assigning to a BP neural network system, and training the training sample in the step (1) by using the BP neural network system to obtain a new generation population;
(4) judging whether the new generation population in the step (3) tends to a stable stage, and if so, optimizing a BP neural network by adopting a Simulated Annealing (SA) algorithm to obtain an optimal solution; if not, returning to the step (3) to train the BP neural network prediction model by taking the new generation population as a training sample, and optimizing the BP neural network connection weight and the threshold by using a Genetic Algorithm (GA);
(5) giving the optimal solution which tends to be stable and optimized in the step (4) to a BP neural network system to update the weight and the threshold value, and initially constructing a blasting blockiness prediction model;
(6) inputting the prediction sample obtained in the step (1) into the blasting block degree prediction model constructed in the step (5), and carrying out error analysis on an output result; if the error exceeds the set threshold, returning to the step (3) to adjust the genetic iteration times of the Genetic Algorithm (GA) to continuously optimize the obtained connection weight and the threshold, and if the error does not exceed the set threshold, completing the blasting block degree prediction model of the engineering field;
(7) and (4) predicting the blasting block degree of the engineering site by using the blasting block degree prediction model finished in the step (6).
2. The method for predicting blasting blockiness based on the SA-GA-BP algorithm according to claim 1, wherein the method comprises the following steps: blasting lumpiness influence factors in the step (1) comprise step height H, drilling load B, spacing S, row spacing B, aperture D and hole depth H1And stuffing h2A chassis resistance line w, a total medicine loading quantity Q, unit consumption Q, an elastic modulus E and a field block size XBAnd petrophysical mechanical properties.
3. The method for predicting blasting blockiness based on the SA-GA-BP algorithm according to claim 1, wherein the method comprises the following steps: the optimization of the BP neural network by the genetic simulated annealing algorithm comprises the following specific steps:
1) the step height to drilling load ratio H/B, the spacing to load ratio S/B, the load to aperture ratio B/D, the stemming to load ratio T/B and the powder factor P of the new generation populationfRock modulus of elasticity E and field bulk size XBThe data form a seven-dimensional vector, an initial population is randomly generated according to the individual vector and the individual is coded according to the networkConstructing a fitness function of the error;
2) calculating the fitness value of the population according to the fitness function relation, and arranging the individuals of the new generation of population according to the sequence of the fitness values from small to large;
3) evenly dividing the new generation population into three levels according to the size of the fitness value, namely dividing the new generation population into three levels of good, medium and poor, and then randomly sampling and selecting individuals in the three levels of good, medium and poor according to the proportion of 5:3:2 to form a new population A;
4) carrying out self-adaptive cross and variation treatment on the population A: with probability P1Recombining the individuals to generate a new individual a by the probability P2Carrying out mutation processing on the new individual a to obtain a new individual b so as to pass through the self-adaptive probability P1Eliminating the individual with gene mutation to obtain population B;
5) judging the stability of the population B, and if the population is stable, entering the optimization operation of the step 6); if the population is unstable, returning to the step 3) to re-sample and select individuals and execute genetic operation;
6) taking the optimal individual in the stable population as the initial solution S of a Simulated Annealing (SA) algorithm0And generating a new solution S using the state function1；
7) Taking the fitness function as a target evaluation function of the simulated annealing operation, and carrying out S) on the initial solution of the step 6)0And new solution S1Evaluating, selecting whether to accept a new solution according to a Metropolis principle, and if so, outputting an optimal solution; if not, cooling according to the temperature decay function, returning to the step 6) to regenerate the new solution S1；
8) Decoding the optimal solution obtained in the step 7), and updating the BP neural network connection weight and the threshold to obtain an optimal weight threshold prediction model.
4. The method for predicting blasting blockiness based on the SA-GA-BP algorithm according to claim 1, wherein the method comprises the following steps: and (4) the threshold initially set in the step (6) is not more than 10%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110107508.1A CN112800673A (en) | 2021-01-27 | 2021-01-27 | Method for predicting blasting block degree based on SA-GA-BP algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110107508.1A CN112800673A (en) | 2021-01-27 | 2021-01-27 | Method for predicting blasting block degree based on SA-GA-BP algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112800673A true CN112800673A (en) | 2021-05-14 |
Family
ID=75812006
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110107508.1A Pending CN112800673A (en) | 2021-01-27 | 2021-01-27 | Method for predicting blasting block degree based on SA-GA-BP algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112800673A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115630257A (en) * | 2022-12-19 | 2023-01-20 | 中南大学 | Blasting funnel volume prediction method |
| CN117669393A (en) * | 2024-02-01 | 2024-03-08 | 昆明理工大学 | Blasting block uncertainty prediction method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778469A (en) * | 2013-01-23 | 2014-05-07 | 辽宁工程技术大学 | Blasting scheme selection method based on neural network optimization genetic algorithm |
| CN106650052A (en) * | 2016-12-06 | 2017-05-10 | 武汉长江仪器自动化研究所有限公司 | Artificial neural network based ingredient blasting parameter intelligent-design method |
| CN107918712A (en) * | 2017-11-28 | 2018-04-17 | 玉溪矿业有限公司 | The construction method of two step open stope afterwards filling large volume strength of filling mass models |
| CN109284818A (en) * | 2018-09-07 | 2019-01-29 | 北方爆破科技有限公司 | A kind of blasting vibration control prediction technique based on accident tree and genetic algorithm |
| CN110232444A (en) * | 2019-06-17 | 2019-09-13 | 武汉轻工大学 | Optimization method, device, equipment and the storage medium of geology monitoring BP neural network |
| CN111062113A (en) * | 2018-12-03 | 2020-04-24 | 湘潭大学 | Novel stope mining blasting parameter comprehensive optimization method under complex filling body condition |
| CN111259601A (en) * | 2020-01-16 | 2020-06-09 | 南华大学 | Blasting blockiness prediction method, device and medium based on random GA-BP neural network group |
-
2021
- 2021-01-27 CN CN202110107508.1A patent/CN112800673A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103778469A (en) * | 2013-01-23 | 2014-05-07 | 辽宁工程技术大学 | Blasting scheme selection method based on neural network optimization genetic algorithm |
| CN106650052A (en) * | 2016-12-06 | 2017-05-10 | 武汉长江仪器自动化研究所有限公司 | Artificial neural network based ingredient blasting parameter intelligent-design method |
| CN107918712A (en) * | 2017-11-28 | 2018-04-17 | 玉溪矿业有限公司 | The construction method of two step open stope afterwards filling large volume strength of filling mass models |
| CN109284818A (en) * | 2018-09-07 | 2019-01-29 | 北方爆破科技有限公司 | A kind of blasting vibration control prediction technique based on accident tree and genetic algorithm |
| CN111062113A (en) * | 2018-12-03 | 2020-04-24 | 湘潭大学 | Novel stope mining blasting parameter comprehensive optimization method under complex filling body condition |
| CN110232444A (en) * | 2019-06-17 | 2019-09-13 | 武汉轻工大学 | Optimization method, device, equipment and the storage medium of geology monitoring BP neural network |
| CN111259601A (en) * | 2020-01-16 | 2020-06-09 | 南华大学 | Blasting blockiness prediction method, device and medium based on random GA-BP neural network group |
Non-Patent Citations (11)
| Title |
|---|
| 冷智高，等: "BP神经网络在爆破振动中的研究与应用", 《有色金属（矿山部分）》 * |
| 刘阳，等: "运用随机森林和GA_BP神经网络预测岩石爆破块度", 《矿业研究与开发》 * |
| 史秀志，等: "基于PCA-BP神经网络的岩石爆破平均粒径预测", 《爆破》 * |
| 张士科等: "基于遗传BP神经网络的煤矿爆破振动特征参量预测", 《煤炭科学技术》 * |
| 李明洲等: "基于SA-GA-BP的某爆破扫雷器电液伺服系统建模", 《兵工自动化》 * |
| 王小川，等: "《MATLAB神经网络43个案例分析》", 31 August 2013, 北京航空航天大学出版社 * |
| 王建国，等: "露天煤矿爆破振动的BP神经网络预测", 《河南理工大学学报（自然科学版）》 * |
| 许国根，等: "《最优化方法及其MATLAB实现》", 31 July 2018, 北京航空航天大学出版社 * |
| 赵翔，等: "BP神经网络在爆破块度预测中的应用研究", 《水泥技术》 * |
| 郭钦鹏等: "运用GA-BP神经网络对爆破振动速度预测", 《爆破》 * |
| 陈微微，等: "BP神经网络预测在台阶爆破参数优化的应用", 《采矿技术》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115630257A (en) * | 2022-12-19 | 2023-01-20 | 中南大学 | Blasting funnel volume prediction method |
| CN117669393A (en) * | 2024-02-01 | 2024-03-08 | 昆明理工大学 | Blasting block uncertainty prediction method and system |
| CN117669393B (en) * | 2024-02-01 | 2024-04-19 | 昆明理工大学 | Blasting block uncertainty prediction method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110363344B (en) | Probability integral parameter prediction method for optimizing BP neural network based on MIV-GP algorithm | |
| CN111985796B (en) | Method for predicting concrete structure durability based on random forest and intelligent algorithm | |
| CN112711904A (en) | Blasting vibration characteristic parameter prediction method based on SA-GA-BP | |
| CN109670650B (en) | Multi-objective optimization algorithm-based solving method for cascade reservoir group scheduling model | |
| CN112800673A (en) | Method for predicting blasting block degree based on SA-GA-BP algorithm | |
| CN110348046B (en) | Drilling track multi-target optimization method based on fuzzy comprehensive evaluation | |
| CN112100927A (en) | Prediction method for slope deformation and soft soil foundation settlement based on GA-BP neural network | |
| Ning et al. | GA-BP air quality evaluation method based on fuzzy theory. | |
| CN112733417A (en) | Abnormal load data detection and correction method and system based on model optimization | |
| CN111723516B (en) | Seawater intrusion simulation-optimization method based on adaptive deep neural network substitution model | |
| CN108154003B (en) | Blasting vibration prediction method based on Spark gene expression optimization | |
| CN104732067A (en) | Industrial process modeling forecasting method oriented at flow object | |
| CN113971517A (en) | GA-LM-BP neural network-based water quality evaluation method | |
| CN115048804A (en) | Urban pipe network multi-sensor optimized deployment method | |
| CN111639820A (en) | Energy consumption parameter optimization method and system for cement kiln production | |
| Ferraro et al. | Use of evolutionary algorithms in single and multi-objective optimization techniques for assisted history matching | |
| CN108734349B (en) | Improved genetic algorithm-based distributed power supply location and volume optimization method and system | |
| CN110633868A (en) | Method for predicting properties of oil and gas in exploration well oil testing layer by optimizing neural network through genetic algorithm | |
| CN113032953A (en) | Intelligent optimization method for injection and production parameters of water-drive oil reservoir of multi-well system | |
| CN106503793A (en) | A kind of neural network short-term wind speed forecasting method based on improvement difference algorithm | |
| CN108133286B (en) | Underground water multi-target calculation method based on ground settlement substitution model | |
| Liu et al. | Automatic calibration of numerical models using fast optimisation by fitness approximation | |
| Jin-yue et al. | Application of BP neural network based on GA in function fitting | |
| Chatterjee et al. | Genetic algorithm-based neural network learning parameter selection for ore grade evaluation of limestone deposit | |
| Yao et al. | Based on the genetic algorithm to optimize the BP neural network in the degree of concrete creep prediction model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210514 |