CN113777000A - Dust concentration detection method based on neural network - Google Patents

Dust concentration detection method based on neural network Download PDF

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CN113777000A
CN113777000A CN202111176735.6A CN202111176735A CN113777000A CN 113777000 A CN113777000 A CN 113777000A CN 202111176735 A CN202111176735 A CN 202111176735A CN 113777000 A CN113777000 A CN 113777000A
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李继明
程学珍
许传诺
赵猛
陈坤
冯浩
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Abstract

The invention provides a dust concentration detection method based on a neural network, which relates to the technical field of dust concentration detection, and comprises the steps of configuring a dust concentration detection system; acquiring dust concentration data through a dust concentration sensor; denoising the dust concentration data based on the wavelet threshold, and extracting a characteristic value of the dust concentration data; and taking the extracted characteristic value as an input quantity, carrying out fusion analysis on the dust concentration data through a BP neural network model, and outputting an analysis result. The method reduces the fusion error in the dust concentration detection, improves the fusion precision, effectively improves the convergence performance, and has certain practical significance in the dust concentration accurate detection project.

Description

Dust concentration detection method based on neural network
Technical Field
The invention relates to the technical field of dust concentration detection, in particular to a dust concentration detection method based on a neural network.
Background
Dust refers to solid particles suspended in air. The dust has adsorbability, carries germs and can cause various diseases when inhaled into the body. Human beings inhale a large amount of dust, can cause serious injury to respiratory system, pile up in the lung and can lead to pneumoconiosis, serious still can cause diseases such as tuberculosis difficult to cure. The dust has the characteristics of inflammability and explosiveness, and is easy to cause explosive accidents once encountering a fire source.
At present, in the field of dust concentration detection, sampling methods and non-sampling methods are divided according to whether sampling is carried out or not. The sampling method firstly carries out sedimentation and then carries out detection, but the core of the non-sampling method is that the sedimentation operation is not needed for detection. The non-sampling method differs from the sampling method in that it does not require sampling, and it uses the characteristics of dust particles to detect concentration. The sampling method is to take a sample from a dust environment, filter out dust and calculate the concentration by the content of dust particles in the sample. The operation process has a certain time interval, and has an obvious defect that real-time detection cannot be realized; the non-sampling method is a detection method for direct measurement, a mathematical model is established by utilizing the relation between the optical and physical properties of dust particles and the concentration, so as to obtain the dust concentration.
The current sampling method relates to a filter membrane weighing method. The principle of the filter membrane weighing method is simple, and the used instruments mainly comprise a sampler, a balance, a filter membrane and the like. The method is suitable for measuring dust with extremely low to medium concentration.
The current sampling method also involves a piezoelectric crystal method. The principle of the piezoelectric crystal method is to calculate the dust concentration on the crystal surface by the vibration frequency of the crystal when the dust falls on the surface of the piezoelectric crystal. The instrument used by the measuring method has the advantages of simple structure, convenient operation, great time saving and higher sensitivity and precision under general conditions. In special cases, for example, where the apparatus is soiled, the measurement system is quickly saturated by the deposited dust, which requires frequent cleaning of the quartz crystal.
The oscillating balance method in the sampling method is to use an oscillating hollow conical tube in a mass sensor, and a replaceable filter membrane is arranged at the oscillating end of the oscillating hollow conical tube, and the oscillating frequency depends on the characteristics of the conical tube and the mass of the conical tube. When the sampling gas flow passes through the filter membrane, the particulate matters in the sampling gas flow are deposited on the filter membrane, the mass change of the filter membrane causes the change of the oscillation frequency, the mass of the particulate matters deposited on the filter membrane is calculated through the change of the oscillation frequency, and then the mass concentration of the particulate matters in the period is calculated according to the flow, the field environment temperature and the gas pressure. The mass concentration of the semi-volatile nitrate and the organic matter in the aerosol is compensated by adopting the FDMS technology, so that the method has the advantages of high precision and few uncertain influence factors.
For the non-sampling method, a blackness method is involved; the blackness method is a measurement method for observing the blackness of gas to be measured by using the vision of people, is similar to a pH test paper method for measuring the pH value of liquid in a chemical method, and obviously has insufficient accuracy although the use is simple. The identification of the blackness of the smoke by ringer's chart is at the discretion of the observer.
The method increases the fusion error in the dust concentration detection, reduces the fusion precision, and cannot effectively improve the convergence performance.
Disclosure of Invention
According to the dust concentration detection method based on the neural network, provided by the invention, the detection method carries out data fusion on the BP neural network model, the accuracy of dust concentration detection is realized, and the dust concentration detection precision is improved.
The dust concentration detection method based on the neural network comprises the following steps:
preparing a dust concentration detection system;
acquiring dust concentration data through a dust concentration sensor;
denoising the dust concentration data based on the wavelet threshold, and extracting a characteristic value of the dust concentration data;
and taking the extracted characteristic value as an input quantity, carrying out fusion analysis on the dust concentration data through a BP neural network model, and outputting an analysis result.
The method of the present invention further comprises: establishing a BP neural network model process;
s201: determining BP neural network topology according to the acquired dust concentration data;
s202: initializing particle speed, position, individual extremum and global extremum;
s203: selecting a fitness function to evaluate a fitness value for each particle;
s204: assigning a value to each particle; if the assigned value of the particle is larger than the individual optimal solution, updating the individual extreme value;
s205: calculating the speed and the position of the particles according to a preset algorithm, and carrying out mutation operation;
s206: if the iteration times are less than the set maximum value or if the error parameter is less than the set error value, returning to the third step;
s207: and distributing the obtained optimal weight and the threshold value to a BP neural network model for training and learning by utilizing an improved particle swarm optimization.
According to the technical scheme, the invention has the following advantages:
in the method, in order to avoid the defects that the BP neural network is trapped in a local minimum value and the training time is too long, the BP algorithm is optimized and weight is adjusted by adopting a simulated annealing algorithm. In order to solve the problems of low later-stage convergence efficiency and poor local search capability of a standard particle swarm algorithm, the search dimension is adjusted by introducing self-adaptive mutation particles by adjusting the inertia weight and the learning factor parameters of the particle swarm, and an improved PSO-BP dust concentration fusion model is established. The method reduces the fusion error in the dust concentration detection, improves the fusion precision, effectively improves the convergence performance, and has certain practical significance in the dust concentration accurate detection project.
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In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description will be briefly introduced, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a flow chart of a dust concentration detection method based on a neural network;
FIG. 2 is a flow chart of establishing a BP neural network model;
FIG. 3 is a schematic diagram of simulated annealing algorithm optimization;
FIG. 4 is a flow chart of optimizing BP algorithm and adjusting weight by using simulated annealing algorithm;
FIG. 5 is a graph of inertial weight change;
FIG. 6 is a graph of learning factor variation;
FIG. 7 is a particle swarm optimization diagram;
FIG. 8 is a topological structure diagram of a three-layer BP neural network model;
FIG. 9 is a flowchart of an embodiment of building a BP neural network model;
FIG. 10 is a flow chart of genetic algorithm optimized BP neural network;
FIG. 11 is a schematic view of a dust concentration detection system;
FIG. 12 is a diagram of the results of dust simulation for an SA-BP neural network;
FIG. 13 is a dust simulation diagram of a PSO-BP neural network;
FIG. 14 is a dust simulation diagram of a GA-BP neural network;
fig. 15 is a network error graph of three BP fusion models.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The units and algorithm steps of each example described in the embodiment disclosed in the neural network-based dust concentration detection method provided by the invention can be realized by electronic hardware, computer software or a combination of the two, and in order to clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functions in the above description. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the dust concentration detection method based on the neural network provided by the invention, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The dust concentration detection method based on the neural network, as shown in fig. 1, comprises the following steps:
s101, configuring a dust concentration detection system;
s102, acquiring dust concentration data through a dust concentration sensor;
s103, denoising the dust concentration data based on the wavelet threshold, and extracting a characteristic value of the dust concentration data;
the collected dust concentration data contains a large amount of noise, and signals need to be denoised, and wavelet denoising is adopted in the invention. The method for denoising the dust concentration data by the wavelet threshold comprises the following steps: acquiring noise-containing dust concentration data; selecting a wavelet function and the number of decomposition layers; decomposing the wavelet and extracting decomposition coefficients of each layer; reconstructing the wavelet; and obtaining denoised dust concentration data.
In the present invention, the characteristic value of the extracted dust concentration data includes: mean, effective value, rectified mean; the mean value is a direct current component belonging to the electrostatic current signal, and the expression is as follows:
Figure BDA0003295438100000041
wherein x isiRepresenting a discrete data unit and n representing a discrete data length.
The effective value (RMS) is an important index for measuring the current intensity, and signals generated when a large amount of dust particles pass through the sensitive element are superposed, so that the effective value of the current signal can be considered to reflect the concentration of the dust to a certain extent. The effective value of the signal is defined as the root mean square value of the current signal in one frame, and is expressed as:
Figure BDA0003295438100000051
wherein x isiRepresenting a discrete data unit and n represents a discrete data length.
The rectified mean value (ARV) is an average value of absolute values of signals, and is also an average value of signals after full-wave rectification, and is expressed as:
Figure BDA0003295438100000052
wherein x isiRepresenting a discrete data unit and n represents a discrete data length.
The data obtained by extracting the characteristic values by the above method are shown in table 4.1, and the left first column of the dust concentration values is a GCG1000 type sensor.
TABLE 4.1 characteristic value table for electrostatic induction signal extraction of sensor
Figure BDA0003295438100000053
Figure BDA0003295438100000061
And S104, taking the extracted characteristic value as an input quantity, carrying out fusion analysis on the dust concentration data through a BP neural network model, and outputting an analysis result.
In the implementation process of the invention, a BP neural network model can be established in advance, and the specific establishment process is as follows; as shown in fig. 2:
s201: determining BP neural network topology according to the acquired dust concentration data;
s202: initializing particle speed, position, individual extremum and global extremum;
s203: selecting a fitness function to evaluate a fitness value for each particle;
s204: assigning a value to each particle;
if the assigned value of the particle is larger than the individual optimal solution, updating the individual extreme value;
s205: calculating the speed and the position of the particles according to a preset algorithm, and carrying out mutation operation;
s206: if the iteration times are less than the set maximum value or if the error parameter is less than the set error value, returning to the third step;
s207: and distributing the obtained optimal weight and the threshold value to a BP neural network model for training and learning by utilizing an improved particle swarm optimization.
In the process of establishing the BP neural network model, optimizing and weight adjusting a BP algorithm by adopting a simulated annealing algorithm;
the simulated annealing algorithm is a probability-based optimization algorithm, a BP neural network is improved by adopting the simulated annealing algorithm, the weight and the threshold of BP are optimized, and an optimization block diagram is shown in FIG. 3.
When the simulated annealing algorithm is adopted to optimize the BP algorithm and adjust the weight, clear network parameters need to be set. The network parameters mainly refer to the number of network layers, hidden layer extraction, transfer functions, algorithm selection and the like, and the parameters determine the performance of the model.
In the invention, a D-dimension search space is provided, a plurality of randomly distributed initial particles are arranged in the search space, the initial particles have respective initial speeds and initial positions, and the optimal solution of the particles is obtained. Group X ═ X1，x2，......，xdWhere the position of the ith particle may be expressed as X ═ Xi1，xi2，......，xidThat is, the corresponding moving speed may be denoted as V (1, 2)i＝{vi1，vi2，...，vidAfter k iterations, the optimal position searched by the particle i is (P)i＝{Pi1，Pi2，...，PidAnd (i) 1, 2, n) the global optimal position in the space searched by the particle swarm is (P)g＝{Pg1，Pg2，...，Pgd})(d＝1，2，...，n)[50]When two optimal positions are searched for
The new velocity and the new position of the particle are iterated again according to equations (2.12) and (2.13).
Figure BDA0003295438100000071
Figure BDA0003295438100000072
In the formula, omega is an inertia factor; c. C1、c2Is an acceleration factor
r1、r2Is a random number and takes the value of (0, 1).
In the invention, in order to select a proper node, the number of nodes of the hidden layer is determined by an equation (3.4).
In determining the number of hidden layers, two hidden layers are provided in consideration of improving efficiency.
The invention selects to use sigmoid activation function in the hidden layer, and purelin linear transfer function in the output layer.
An algorithm combining adaptive learning rate and simulated annealing algorithm is used herein. The setting of the network weight affects the convergence, convergence speed and error accuracy of the algorithm, and generally, the initial weight is set to a random number of (-1, 1). The expected error is set to 0.01 and the maximum number of iterations is set to 1000.
The BP neural network model related by the invention adopts a four-layer neural network structure and two hidden layers. Adjacent layers are connected by weights. After the network structure is set, the number of nodes in the four-layer BP neural network can still be adjusted in the training process so as to improve the fusion precision of the model. The input layer is a characteristic parameter extracted by the electrostatic induction current signal, and the output layer is a dust concentration value. The number of hidden layer nodes is determined by equation (3.4):
Figure BDA0003295438100000081
wherein n isYRepresenting the number of hidden layer nodes, ninputNumber of input layers, noutputDenotes the number of output layers, a is [1, 10 ]]A constant value in between.
The basic learning rate and the decay rate generate a large change rate in each training period, thereby affecting the training result. And the basic learning rate and the attenuation rate are adjusted by adopting a self-adaptive adjusting method, so that the convergence effect of the neural network is ensured. The formula is as follows:
Figure BDA0003295438100000082
here, z (k) represents the sum of squared errors of the kth training.
In order to avoid the BP algorithm from falling into a local minimum value, the simulation annealing algorithm is adopted to optimize the BP neural network model and adjust the weight, so that the generalization capability of the model is improved. When the system state tends to be stable, the local optimal solution can be considered to be reached, the simulated annealing algorithm can jump out of the local optimal solution with a certain probability, and the global optimal solution of the objective function is searched. The state transition probability criterion (Metropolis criterion) is as follows:
Figure BDA0003295438100000083
where K represents the boltzmann constant. The probability of state transition decreases with decreasing temperature. The process of optimizing the BP algorithm and adjusting the weight value by using the simulated annealing algorithm is shown in fig. 4.
In the invention, in the process of establishing the BP neural network model, self-adaptive mutation particles are introduced to adjust the search dimension by adjusting the inertia weight of the particle swarm and the parameters of the learning factor, so that the BP neural network model is established.
Specifically, in order to solve the problems of low later stage convergence efficiency and poor local search capability of the standard particle swarm algorithm, the improved particle swarm algorithm is applied to realize global search, so that the performance of the overall algorithm is improved.
For the improvement of the inertial weight of the present invention, the weight function can adjust the global and local search capability of the algorithm. In a standard particle swarm algorithm, the inertia weight is decreased along a straight line, so that the inertial weight has strong global exploration capability in the initial stage of iteration and strong local search capability in the later stage, but the phenomenon of 'precocity' is very easy to occur. A method is employed herein to improve the inertial weight coefficient. The change in inertial weight is shown in fig. 5.
In the initial search stage, the inertia weight coefficient is reduced nonlinearly, so that the algorithm has stronger capability of global search in the initial search stage and enters local search as soon as possible. After k iterations, the inertia weight coefficient begins to decrease linearly, so that the algorithm can stably find the optimal solution. The algorithm is adjusted as follows:
Figure BDA0003295438100000091
where t denotes the number of iterations, wmaxAnd wminRespectively representMaximum sum of inertia weight coefficients l1(t) denotes a non-linear function,/2(t) represents a linear function, and d represents an initial inertial weight after the initial search. l1(t) and minimum value, l2The value of (t) is derived as follows:
Figure BDA0003295438100000095
Figure BDA0003295438100000092
wherein t ismaxIs the maximum number of iterations.
In order to obtain the diversity of particles in the initial search stage and converge to the global optimal solution as soon as possible in the later stage, the invention uses the tangent function to dynamically adjust the parameter c by analyzing the influence of the change of the learning factor1And c2To better balance global and local searches. The tangent function is expressed as follows:
Figure BDA0003295438100000093
Figure BDA0003295438100000094
parameter c1And c2The curve of (2) changes as shown in fig. 6. As can be seen from the figure, at the initial stage of the search,
c1ratio c2Large, each particle focuses on the historical information of the individual to ensure diversity. At the later stage of the search, c1Decrease, and c2Increasing the number of particles to focus more on population information to maintain fast convergence.
The invention can also optimize the self-adaptive mutation particle swarm. In the iterative process, the standard particle swarm algorithm is easy to fall into a local extreme value, and the population loses the whole searching capability in the process. By referring to the 'mutation' operation of the genetic algorithm, one dimension of a particle can be mutated, the position of the particle can be adjusted with a certain probability, and the particle can enter other regions to continue searching. Therefore, the search range can be effectively expanded, and the global optimal solution of the algorithm can be obtained. This is the basic idea behind adaptive variant PSO, whose formula is as follows:
p(i，k)＝5×rand，x＞0.95 (3.9)
where p (i, k) represents a k-dimensional mutation operation of i particles in the population. The variation occurs when the random number x between 0 and 1 is greater than 0.95 and rand is a random value between 0 and 1.
According to the invention, a particle swarm algorithm is adopted to improve the BP neural network, the weight and the threshold of a BP neural network model are optimized, and an optimization block diagram is shown in FIG. 7.
The invention determines that the BP neural network has a three-layer structure, and determines the neuron n of an input layer according to the quantity of input and output1And output layer neurons n3. Second, the hidden layer n is determined based on an empirical formula2The number of neurons in (a), the minimum error of equation (3.10) is obtained.
Figure BDA0003295438100000101
Figure BDA0003295438100000102
Here, ηkIs the threshold value of the output layer, θjIs the threshold of the hidden layer. The connection weight between the input layer and the hidden layer is defined as wij. The connection weight of the hidden layer and the output layer is defined as Vjk，f0Sigmoid activation function for hidden layer, f1Is the linear transfer function of the output layer. One group has 3 sensors, each of which extracts 3 characteristic values, which are respectively a mean value, a rectified mean value and an effective value, and totally has 9 characteristic values, so that the number of nodes of an input layer is 9, a dust concentration value is output, the number of nodes of an output layer is 1, and the applied neural network topology is shown in fig. 8.
Each one of which isThe Mean Square Error (MSE) produced by the network training set is considered as the approximate fitness function used to calculate the fitness value, equation (3.12), and the minimum | error value E is calculated based on the fitness function f (x) E (x)min。
Figure BDA0003295438100000111
In the above formula, yiAnd
Figure 1
the target value and the fusion value, respectively. The smaller the MSE, the modelFusion valueThe more accurate. The invention updates the V of the particles under different componentsiUntil the training error is less than EminOr the number of iterations reaches tmax. If the error after training does not satisfy EminWe can adjust the weights and thresholds to satisfy this condition. According to the dust concentration detection method provided by the invention, based on the limitation of a BP network, a particle swarm optimization is introduced for improvement, the improved particle swarm optimization optimizes the change strategy of the inertia weight and the learning factor, the global search capability in the early stage is ensured, and the optimal solution can be quickly converged in the later stage. And a self-adaptive variation algorithm is introduced in the searching process, so that the particles are prevented from being trapped in local optimum.
The BP neural network model is established by applying the BP network topology and the optimized particle swarm of the present invention, and a model establishing flow as an embodiment of the present invention is shown in fig. 9.
In the invention, the realization of GA optimization BP neural network is used as the optimization effect contrast of PSO-BP. The weight and the threshold of the BP neural network are optimized by selecting a genetic algorithm, the fuzzy solution from the beginning approaches to the accurate solution continuously, and a neural network model is constructed by utilizing the copy, the intersection and the variation of genetic factors.
The specific implementation steps are that the optimal individual in the genetic algorithm is decoded, population initialization parameters are set, a fitness function is set, then the optimal weight and threshold are obtained by utilizing the combination optimization of initial values, and the optimal weight and threshold obtained by solving are substituted into the process again for learning training and simulation prediction. By the method, the genetic algorithm endows the BP neural network with the optimal weight and threshold, and the GA-BP model is built, as shown in FIG. 10.
In the invention, in order to better evaluate and improve the precision of the BP neural network model for data fusion, the following indexes are used for evaluation:
(1) mean absolute error:
Figure BDA0003295438100000113
(2) mean square error:
Figure BDA0003295438100000121
(3) mean absolute percentage error:
Figure BDA0003295438100000122
wherein x isiIn order to be the true value of the value,
Figure BDA0003295438100000123
as a fusion value, eiFor fusion error, N is the number of samples, MAE, MSE and MAPE are selected as evaluation indexes based on a PSO-BP neural network and a GA-BP neural network, and the fusion performance of the BP neural network is verified and improved.
In the invention, in order to avoid the defects that the BP neural network is trapped in a local minimum value and the training time is too long, a simulated annealing algorithm is adopted to optimize the BP algorithm and adjust the weight. In order to solve the problems of low later-stage convergence efficiency and poor local search capability of the standard particle swarm algorithm, the search dimension is adjusted by introducing self-adaptive mutation particles by adjusting the inertia weight and the learning factor parameters of the particle swarm, and a BP neural network model is established.
Based on the dust concentration detection method based on the neural network, the invention carries out experimental verification, in particular to configure a dust concentration detection system; the specific detection process comprises the following steps: the method comprises the steps of firstly, acquiring static induction signals by using a rod-shaped static sensor on a mine dust environment detection platform, then denoising the signals, extracting characteristic values, and finally fusing the extracted characteristic values in a BP neural network model to obtain the dust concentration.
Fig. 11 is a schematic view of the dust concentration detection system. The adopted fan is an axial flow fan. The fan parameters are as follows: the air volume is 18500m3H; the wind pressure is 327 Pa; the revolution number is 1450 r/min; the power is 3 KW; the voltage is three-phase AC 380V. Because the aerosol particles have the electrification property, the experiment simulates the dust environment in the pipeline through the aerosol generator device. In order to make the measuring result more accurate, adjust the dust speed through adjustment air compressor rotational speed and dust generating device, make the abundant homodisperse of dust ability in the experiment pipeline. During the experiment, open simulation tunnel power earlier, start fan and dust purification collection device, then open static induction sensor and begin the signal of gathering, open dust generating device after the tunnel internal environment is stable and test. After the experiment is finished, the aerosol is collected through the dust collector, so that cyclic utilization is realized.
Install 3 bar-shaped electrostatic sensors in the pipeline respectively, utilize the dust remover to clear away the dust in the pipeline after the experiment, avoid producing adverse effect to the experimental result.
The invention uses the rod-shaped electrode to obtain the electrostatic signal data by using the electrostatic induction principle. When the charged dust passes through the vicinity of the electrode, the electrode can induce corresponding charges, the charge quantity is in positive correlation with the electric quantity charged by the particles, and a static induced current signal is obtained through a measuring circuit. The further processing of the current signal can analyze the relation between the parameter quantity and the dust concentration, and then the more accurate dust concentration can be obtained. And data acquisition is realized through a computer terminal. And the data processing and transmission process of the data is visually displayed by the upper computer, the upper computer programming is carried out by utilizing LabVIEW software, and the data storage and analysis are carried out on the acquired signals through an internal program.
Acquiring dust concentration data through a dust concentration sensor; denoising the dust concentration data based on the wavelet threshold, and extracting a characteristic value of the dust concentration data; and taking the extracted characteristic value as an input quantity, carrying out fusion analysis on the dust concentration data through a BP neural network model, and outputting an analysis result.
In the invention, the sampling time of the equipment is set to be 6s, the frequency in the system is set to be 1000Hz, and signal acquisition is carried out by means of LabVIEW. The static signals collected by 3 sensors under different concentrations are counted, the data collected by the 3 sensors at one time are a group, and after invalid data and data with larger errors are deleted, 200 groups of data are collected. The electrostatic signal is subjected to operations of wavelet threshold denoising and feature extraction in sequence. Three aspects of characteristic values are extracted, including an effective value, an average value, and a rectified average value of the electrostatic current. The method adopts three models of an improved BP neural network, a GA-BP neural network and a PSO-BP neural network to perform simulation detection on the dust concentration by applying the characteristic value of the electrostatic induction signal.
Extracting 150 groups of obtained test data as training samples and 100 groups of test samples, setting the values of all parameters in a reasonable range in the optimization process of three BP algorithms, wherein each group comprises 3 sensors, and three characteristic values are extracted from each group, so that the number of nodes of an input layer is 9, the number of nodes of an output layer is 1, calculating the number of nodes of a hidden layer to be [4, 13] according to experience, and obtaining the minimum iteration error when the number of nodes of the hidden layer is 11 through multiple times of simulation. Thus determining the network structure form of 9-11-1.
The hidden layers of the three BP neural network models adopt sigmoid functions, the output layer adopts purelin functions, the network training function adopts a tractlm function, and the performance function adopts an MSE function. The maximum learning times of the three BP neural networks are set to be 1000 times, and the target error is 0.001. Since the traditional BP simulation error is too large in section 4.2, this section mainly performs fusion simulation on three improved BP neural networks.
Modeling and simulating a BP neural network model based on a Matlab/Simulink environment, and fusing and analyzing sample data by using an SA-BP neural network through a BP neural network toolbox carried by Matlab software. The setup parameters are shown in table 5.1 below.
TABLE 5.1 BP neural network parameter settings
Figure BDA0003295438100000151
The dust simulation results of the SA-BP neural network are shown in fig. 12.
For the PSO-BP neural network model, modeling and simulation are carried out on the PSO-BP neural network model, sample data are fused by using a BP neural network data box carried by Matlab, and parameter setting in the algorithm is shown in a table 5.2.
TABLE 5.2 PSO-BP neural network parameter values
Figure BDA0003295438100000152
The result of the dust simulation of the PSO-BP neural network is shown in fig. 13.
For the GA-BP neural network model:
the BP parameter setting of the genetic algorithm is the same as that in the PSO-BP, modeling and simulation are carried out on a GA-BP neural network model based on a Matlab/Simulink environment, sample data are fused by utilizing an improved BP neural network, and the parameter setting in the algorithm is shown in a table 5.3.
TABLE 5.3 values of various parameters of GA-BP neural network
Figure BDA0003295438100000161
The results of the dust simulation of the GA-BP neural network are shown in FIG. 14.
By comprehensive analysis, the network error curves of the three BP fusion models are shown in fig. 15. It can be seen from fig. 15 that when the convergence error accuracy is set to 0.001, the convergence frequency of the SA-BP neural network to achieve this accuracy is 60 times, the convergence frequency of the GA-BP neural network to achieve this accuracy is 50 times, and the convergence frequency of the BP neural network after particle swarm optimization is 10 times, the target error accuracy is achieved, so that it can be seen that the convergence performance of the PSO-BP is far superior to that of the other two neural network models.
The invention also analyzes the experimental data results. The test set was trained and simulated using the above three BP neural networks, and 10 groups were selected and statistically calculated as shown in table 5.4.
TABLE 5.4 test set Standard values and fusion value data sheet
Figure BDA0003295438100000171
As can be seen from the indexes of the three evaluation model accuracies of MAE, MSE, and MAPE in table 5.4, the evaluation parameters MAE, MSE, and MAPE of the dust concentration fusion error of PSO-BP are respectively 0.297, 0.328, and 2.749%, and it can be found by comparing with the other two fusion neural network models that the three error evaluation indexes are far smaller than those of the other two models, where MAE is reduced by 47.70% and 38.70%, MSE is reduced by 75.70% and 54.10%, and MAPE is reduced by 1.728% and 1.400%, respectively, which indicates that the PSO-BP neural network model reduces the fusion error in the dust concentration simulation and improves the fusion accuracy. Compared with the improved BP neural network and GA-BP fusion algorithm, errors of various performance indexes in the algorithm fusion result of the BP neural network model after particle swarm optimization are smaller than those of the other 2 models, the fusion error is minimum in dust concentration detection, and the fusion value is most accurate.
It can be seen that the fusion precision of the verification model when the experiment platform is set up is generally lower than that of the algorithm simulation, and the analysis is mainly due to the reduction of precision caused by data errors acquired by the experiment platform.
The simulation comparison results of the graphs are analyzed, and the fact that the fusion effect of the PSO-BP neural network model is superior to that of the other two improved BP fusion models can be obtained. The BP neural network model optimized by the particle swarm optimization has better fusion effect on dust concentration simulation, smaller error and the fused dust concentration value is closer to the true value.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1. A dust concentration detection method based on a neural network is characterized by comprising the following steps:
preparing a dust concentration detection system;
acquiring dust concentration data through a dust concentration sensor;
denoising the dust concentration data based on the wavelet threshold, and extracting a characteristic value of the dust concentration data;
using the extracted characteristic value as an input quantity;
establishing a BP neural network model process;
s201: determining BP neural network topology according to the acquired dust concentration data;
s202: initializing particle speed, position, individual extremum and global extremum;
s203: selecting a fitness function to evaluate a fitness value for each particle;
s204: assigning a value to each particle; if the assigned value of the particle is larger than the individual optimal solution, updating the individual extreme value;
s205: calculating the speed and the position of the particles according to a preset algorithm, and carrying out mutation operation;
s206: if the iteration times are less than the set maximum value or if the error parameter is less than the set error value, returning to the third step;
s207: distributing the obtained optimal weight and threshold to a BP neural network model for training and learning by using an improved particle swarm algorithm;
and performing fusion analysis on the dust concentration data through a BP neural network model, and outputting an analysis result.
2. The neural network-based dust concentration detection method according to claim 1, further comprising:
in the process of establishing the BP neural network model, optimizing and weight adjusting a BP algorithm by adopting a simulated annealing algorithm;
when the system state tends to be stable, the local optimal solution is considered to be reached, the local optimal solution can be jumped out by using a simulated annealing algorithm according to a preset probability, and a global optimal solution of the objective function is searched; the state transition probability criterion is as follows:
Figure FDA0003295438090000021
where K represents the boltzmann constant.
3. The dust concentration detection method based on the neural network as claimed in claim 1, wherein in the process of establishing the BP neural network model, the BP neural network model is established by adjusting the inertia weight of the particle swarm and the parameters of the learning factor and introducing self-adaptive mutation particles to adjust the search dimension.
4. The dust concentration detection method based on the neural network as claimed in claim 1, wherein in the process of establishing the BP neural network model,
decoding the optimal individual in the genetic algorithm, setting population initialization parameters, and setting a fitness function;
obtaining an optimal weight and a threshold value by utilizing the combined optimization of the initial values, and substituting the optimal weight and the threshold value obtained by solving into the flow again to carry out learning training and simulation prediction; and (3) endowing the BP neural network with the optimal weight and threshold by the genetic algorithm to complete the construction of the GA-BP model.
5. The neural network-based dust concentration detecting method according to claim 1,
and (3) taking the average absolute error, the mean square error and the average absolute percentage error as evaluation indexes of the BP neural network and the GA-BP neural network to verify the fusion performance of the BP neural network model.
6. The neural network-based dust concentration detecting method according to claim 1,
in step five, the velocity and position of the particle are calculated according to equations (2.12), (2.13), (3.4), (3.7) and (3.8):
Figure FDA0003295438090000031
Figure FDA0003295438090000032
in the formula, omega is an inertia factor; c. C1、c2Is an acceleration factor; r is1、r2Is a random number and takes the value of (0, 1);
Figure FDA0003295438090000033
where t denotes the number of iterations, wmaxAnd wminRespectively representing the maximum and minimum values of the inertial weight coefficient, l1(t) denotes a non-linear function,/2(t) represents a linear function, d represents an initial inertial weight after an initial search;
the tangent function is expressed as follows:
Figure FDA0003295438090000041
Figure FDA0003295438090000042
carrying out mutation operation according to a formula (3.9);
p(i，k)＝5×rand，x＞0.95 (3.9)
where p (i, k) represents a k-dimensional mutation operation of i particles in the population.
7. The dust concentration detection method based on the neural network as claimed in claim 1, wherein the denoising processing mode of the wavelet threshold on the dust concentration data comprises:
acquiring noise-containing dust concentration data;
selecting a wavelet function and the number of decomposition layers;
decomposing the wavelet and extracting decomposition coefficients of each layer;
reconstructing the wavelet;
and obtaining denoised dust concentration data.
8. The neural-network-based dust concentration detection method according to claim 1, wherein extracting the characteristic value of the dust concentration data includes: mean, effective value, rectified mean;
the mean extraction mode bit expression is:
Figure FDA0003295438090000043
wherein x isiRepresenting a discrete data unit, n representing a discrete data length;
the extraction mode of the effective value is as follows:
Figure FDA0003295438090000051
wherein x isiRepresenting a discrete data unit, n representing a discrete data length;
the extraction mode of the rectified average value is as follows:
Figure FDA0003295438090000052
wherein x isiRepresenting a discrete data unit and n represents a discrete data length.
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