CN104463359A - Dredging operation yield prediction model analysis method based on BP neural network - Google Patents
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Abstract
The invention discloses a dredging operation yield prediction model analysis method based on a BP neural network. The dredging operation yield prediction model analysis method based on the BP neural network comprises the following steps that (1) data information influencing dredging operation yield factor variables is collected, p influence factors are determined, and a sample matrix is listed, wherein p is a positive integer; (2) pretreatment is conducted on sample data; (3) a network is established, and a training sample and a testing sample are determined; (4) the established network is trained according to the training sample; (5) according to the testing sample, the established network is tested; (6) the performance of the network is estimated by computing the offset condition between a predicated value and a true value. By the adoption of the dredging operation yield prediction model analysis method based on the BP neural network, a nonlinear mapping function from input to output is achieved, a nonlinear relationship is established between input and output, and an established model is high in fault-tolerant capacity and high in prediction speed; theoretical basis can be laid for optimization study of dredging operation yield, and the purposes of high efficiency, high yield and low energy consumption can be achieved.
Description
Technical field
The invention belongs to Dredging Technology field, be specifically related to a kind of dredging operation Production Forecast Models analytical approach based on BP neural network.
Background technology
Dredging work is the big event of water conservancy marine traffic engineering.Modern dredging operation mainly relies on hog barge to carry out, and output weighs the major criterion of hog barge efficiency.Along with the intelligentized development of dredging work, hog barge all arranges monitoring device, off-line or on-line monitoring have been carried out to many duty parameters, and by radio communication, these Monitoring Data are sent to monitoring station.Therefore, in long-term production run, hog barge have accumulated and has enriched full and accurate duty parameter data.These data are to the true reflection of hog barge under specific region, specific environment in actual moving process, and having obvious potential value, is important scientific and technological resources, and regrettably these resources but well do not utilize at present.How to utilize the information of containing in data to find the production law in Dredging Process, and utilize the production law found out from data, realizing the Increasing Production and Energy Saving of hog barge, is explore an energy-conservation urgent problem of hog barge at present.Therefore, dredging operation Production Forecast Models is realized significant.
Artificial neural network is exactly a kind of mode of simulation people thinking, and be a Kind of Nonlinear Dynamical System, its characteristic is distributed storage and the concurrent collaborative process of information.Although single neuronic structure is extremely simple, function is limited, and the behavior achieved by network system that a large amount of neuron is formed is extremely colourful.The most generally BP neural network is used in artificial neural network, BP neural network achieves in fact one from the Nonlinear Mapping function being input to output, mathematical theory proves that the neural network of three layers just can approach any non-linear continuous function with arbitrary accuracy, when training, can automatically extract " rule of reason " between outputs, output data by study and adaptively learning content be remembered in the weights of network.In addition, also there is extensive and fault-tolerant ability widely, avoid characteristic factor and differentiate that the complex relationship of target describes, the particularly statement of formula.Network can relation between each input quantity of oneself learning and memory and output quantity.
Summary of the invention
For the deficiency that prior art exists, the object of the invention is to provide a kind of dredging operation Production Forecast Models analytical approach based on BP neural network, utilize existing BP neural net method, hog barge Production Forecast Models is analyzed, for the optimizing research of dredging operation output lays theoretical foundation, thus reach the object of high-level efficiency, high yield, low energy consumption, recovery prediction is carried out to hog barge significant.
To achieve these goals, the present invention realizes by the following technical solutions:
Based on a dredging operation Production Forecast Models analytical approach for BP neural network, comprise the following steps:
Step (1): collect the data information affecting dredging operation yield factors variable, determine p influence factor, list sample matrix; Wherein, p is positive integer;
Step (2): pre-service is carried out to sample data;
Step (3): tectonic network structure, determines training sample and test sample book;
Step (4): according to training sample, trains the network built up;
Step (5): according to test sample book, tests the network built up;
Step (6): network performance is evaluated.
Sample matrix in above-mentioned steps (1) is as follows:
If to p influence factor x
1, x
2... x
pcarried out n observation, output is set as dependent variable Y, and the data matrix of " sample point × variable " type of note independent variable is:
X=(x
ij)
n × p=(x
1, x
2... x
p), i=1,2 ..., n (sample number); J=1,2 ... p (variable number)
In above-mentioned steps (2), sample data pre-service is as follows:
Data prediction comprises missing data process, outlier processing, denoising and normalized.
The method of described missing data process is for removing missing data.
The disposal route similar to missing data can be adopted to abnormal data, namely remove abnormal data.As for the standard of abnormal data, will depending on particular problem, a kind of standard often used in reality is: be greater than 3 times of standard deviations with the deviation of mean value.I.e. 3 σ criterions.
Noise is random error in a measurand or deviation, comprises the value of mistake or departs from the isolated point of expectation.Its disposal route carrys out smoothed data by allowing data adapt to regression function.Here mainly carry smooth function by MATLAB and carry out smoothing processing.
Data normalization process be all data transformations to [0,1] between number, its object eliminates order of magnitude difference between each dimension data exactly, avoids because inputoutput data order of magnitude difference is comparatively large and causes neural network forecast error larger.Adopting more is minimax method, and functional form is as follows:
x
d＝(x
d-x
min)/(x
max-x
min) (1)
Normalized function adopts MATLAB to carry mapminmax function to process.
Create BP neural network in above-mentioned steps (3), determine training sample and test sample book step as follows:
1. network creation
BP network structure define following two important governing principles.
1) for general classification forecasting problem, Three Tiered Network Architecture can well be dealt with problems.
2) in three-layer network, there is such experimental formula in hidden layer neural network number l and input layer number n and output layer neuron number m:
(a is the regulating constant between 1 ~ 10) (2)
Input layer number n and output layer neuron number m depends on the input of training sample respectively, exports the dimension of data.The hidden layer neuron transport function of neural network adopts S type tan tansig (), and the neuronic transport function of output layer adopts function purelin (), and sample training adopts function trainlm ().Assuming that training input amendment matrix is P, training output sample matrix is T, creates network and can use following MATLAB code:
Net＝newff(P,T,l,{‘tansig’,‘purelin’},‘trainlm’)
2. training sample and test sample book are determined
The generalization ability of point pairing neural network of test sample book and number of training has very large impact, if training sample very little, then learn abundant not, the rule of mistake, even appears in the relation between the input and output of very difficult map neural network accurately.If training sample is too many, if the speed of impact training, meanwhile, generalization ability is also not necessarily fine in the application, likely occurs Expired Drugs.Training sample gets 90% of total sample, and test sample book gets 10% of total sample.
In upper described step (4) according to training sample, training step carries out to the network built up as follows:
Network training is a process constantly revising weights and threshold, by training, makes the output error of network more and more less.Training function trainlm () utilizes Levenber-Marquardt algorithm to train network, the optimum configurations by once MATLAB code call trainlm () and network:
%% frequency of training is 1000, training objective position 0.001, and learning rate is 0.1
Net.trainParam.epochs＝1000；
Net.trainParam.goal＝0.001；
LP.lr＝0.1；
%% training network
Net＝train(Net,P,T)
According to test sample book in upper described step (5), testing procedure carries out to the network built up as follows:
Network training well after, need to test network.Assuming that test sample book data matrix is P_test, test MATLAB code is as follows:
Y_output＝sim(Net，P_test)
In above-mentioned steps (6), evaluation is carried out to network performance as follows:
After the test of BP neural network terminates, by the deviation situation of computational prediction value and actual value, can evaluate the generalization ability of network.Here two evaluation indexes selected are relative error and the coefficient of determination, and its computing formula is as follows respectively:
1. relative error is:
2. the coefficient of determination is:
Wherein, q is test sample book number,
for the predicted value of a kth sample;
Y
k(k=1,2 ... q) be the actual value of a kth sample.
Illustrate: 1) relative error is less, show that the performance of model is better;
2) coefficient of determination scope is in [0,1], more close to 1, shows that model performance is better: otherwise, more close to 0, show that the performance of model is poorer.
Draw test sample book number and network exports, the curve map of network expectation value, relative error respectively.
Present invention achieves one and set up the nonlinear relationship between constrained input from the Nonlinear Mapping function being input to output, overcome the drawback that conventional mathematical model can only describe production run qualitatively; Not only fault-tolerant ability is strong for the forecast model set up, predetermined speed is fast, also avoids characteristic factor and differentiates that the complex relationship of target describes, the particularly statement of formula; The optimizing research that can be dredging operation output lays theoretical foundation, reaches the object of high-level efficiency, high yield, low energy consumption, carries out recovery prediction significant to hog barge.
Accompanying drawing explanation
Fig. 1 is dredging Production Forecast Models Establishing process figure of the present invention;
Fig. 2 is dredging recovery prediction value of the present invention and actual comparison result figure;
Fig. 3 is dredging recovery prediction relative error figure of the present invention.
Embodiment
The technological means realized for making the present invention, creation characteristic, reaching object and effect is easy to understand, below in conjunction with embodiment, setting forth the present invention further.
See Fig. 1, in the present embodiment, the analytical approach of cutter suction dredger Production Forecast Models is as follows:
(1) cutter suction dredger Yield Influence Factors has numerous parametric variable, first collects data information, determines situational variables.Cutter suction dredger Yield Influence Factors is as shown in table 1.Output is one dimension dependent variable Y.
Table 1 cutter suction dredger Yield Influence Factors
(2) pre-service is carried out to raw data
Raw data carries out pretreated result:
Can obtain dependent variable after carrying out data prediction to output dependent variable Y and Yield Influence Factors X respectively according to above-mentioned steps (2) is Y ', and independent variable is X '.Its data matrix is:
X '=(x '
ij)
n × 8=(x '
1, x '
2... x '
8) and Y '=(y '
ij)
n × 1=(y '
1)
(3) tectonic network structure, determines training sample and test sample book
Input layer number n=8, output layer neuron number m=1, can obtain hidden layer neuron number according to formula (2) is l=10.
Population sample number is 1603, then number of training gets 1450, and test sample book number gets 153.The hidden layer neuron transport function of neural network adopts S type tan tansig (), and the neuronic transport function of output layer adopts function purelin (), and sample training adopts function trainlm ().Assuming that training input amendment matrix is P, training output sample matrix is T, creates network and can use following MATLAB code:
Net＝newff(P,T,10,{‘tansig’,‘purelin’},‘trainlm’)
(4) according to training sample, the network built up is trained
Training function trainlm () utilizes Levenber-Marquardt algorithm to train network, the optimum configurations by once MATLAB code call trainlm () and network:
%% frequency of training is 1000, training objective position 0.001, and learning rate is 0.1
Net.trainParam.epochs＝1000；
Net.trainParam.goal＝0.001；
LP.lr＝0.1；
%% training network
Net＝train(Net,P,T)
(5) according to test sample book, the network built up is tested
Network training well after, need to test network.Assuming that test sample book data matrix is P_test, test MATLAB code is as follows:
Y_output＝sim(Net，P_test)
(6) network performance is evaluated
According to formula (3) and (4) can obtain, its result as Fig. 2, shown in 3.As can be seen from the figure, relative error remains on 10
-2in, coefficient of determination R
2=0.9024, BP neural network model better performances is described, the prediction of dredging output can be realized preferably.
More than show and describe ultimate principle of the present invention and principal character and advantage of the present invention.The technician of the industry should understand; the present invention is not restricted to the described embodiments; what describe in above-described embodiment and instructions just illustrates principle of the present invention; without departing from the spirit and scope of the present invention; the present invention also has various changes and modifications, and these changes and improvements all fall in the claimed scope of the invention.Application claims protection domain is defined by appending claims and equivalent thereof.
Claims (9)
1., based on a dredging operation Production Forecast Models analytical approach for BP neural network, it is characterized in that, specifically comprise following step:
(1) collect the data information affecting dredging operation yield factors variable, determine p influence factor, list sample matrix, wherein, p is positive integer;
(2) pre-service is carried out to sample data;
(3) create BP neural network, determine training sample and test sample book;
(4) according to training sample, the network built up is trained;
(5) according to test sample book, the network built up is tested;
(6) by the deviation situation of computational prediction value and actual value, network performance is evaluated.
2. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (1), described sample matrix is as follows:
If to p influence factor x
1, x
2... x
pcarried out n observation, output is set as dependent variable Y, and the data matrix of the sample point × variable type of note independent variable is:
X=(x
ij)
n × p=(x
1, x
2... x
p), i=1,2 ..., n (sample number); J=1,2 ... p (variable number).
3. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (2), described sample data pre-service comprises missing data process, outlier processing, denoising and normalized.
4. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 3, is characterized in that, the method for described missing data process is for removing missing data; The method of described outlier processing is for removing abnormal data; The method of described denoising is for carrying out smoothed data by allowing data adapt to regression function, and carrying smooth function by MATLAB carrys out smoothing processing; The method of described normalized is minimax method, and functional form is as follows:
x
d＝(x
d-x
min)/(x
max-x
min)
Normalized function adopts MATLAB to carry mapminmax function to process.
5. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, is characterized in that, in step (3), the method that BP neural network creates is as follows:
In three-layer network, there is following experimental formula in hidden layer neural network number l and input layer number n and output layer neuron number m:
a is the regulating constant between 1 ~ 10
Input layer number n and output layer neuron number m depends on the input of training sample respectively, exports the dimension of data; The hidden layer neuron transport function of neural network adopts S type tan tansig (), and output layer neural transferring function adopts function purelin (), and sample training adopts function trainlm (); Assuming that training input amendment matrix is P, training output sample matrix is T, creates network and can use following MATLAB code:
Net＝newff(P,T,l,{‘tansig’,‘purelin’},‘trainlm’) 。
6. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (3), the defining method of training sample and test sample book is as follows:
Described training sample gets 90% of total sample, and described test sample book gets 10% of total sample.
7. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (4), according to training sample, the method for training the network created is as follows:
Training function trainlm () utilizes Levenber-Marquardt algorithm to train network, the optimum configurations by following MATLAB code call trainlm () and network:
%% frequency of training is 1000, training objective position 0.001, and learning rate is 0.1
Net.trainParam.epochs＝1000；
Net.trainParam.goal＝0.001；
LP.lr＝0.1；
%% training network
Net＝train(Net,P,T) 。
8. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (5), according to test sample book, the method for testing the network built up is as follows:
Network training well after, network is tested, assuming that test sample book data matrix is P_test, then tests MATLAB code as follows:
Y_output＝sim(Net，P_test) 。
9. the dredging operation Production Forecast Models analytical approach based on BP neural network according to claim 1, it is characterized in that, in step (6), the method evaluated network performance is as follows:
Two evaluation indexes selected are relative error and the coefficient of determination, and its computing formula is as follows respectively:
Relative error is:
The coefficient of determination is:
In formula, q is test sample book number,
for the predicted value of a kth sample; y
k(k=1,2 ... q) be the actual value of a kth sample;
Relative error is less, shows that the performance of model is better; Coefficient of determination scope, in [0,1], more close to 1, shows that model performance is better, otherwise, more close to 0, show that the performance of model is poorer.
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Application publication date: 20150325 |