"Analysis of 43 cases of MATLAB neural network": Chapter 41 implementation of customized neural network -- personalized modeling and Simulation of neural network

1. Preface

《MATLAB neural network 43 A case study 》 yes MATLAB Technology Forum （www.matlabsky.com） planning , Led by teacher wangxiaochuan ,2013 Beijing University of Aeronautics and Astronautics Press MATLAB A book for tools MATLAB Example teaching books , Is in 《MATLAB neural network 30 A case study 》 On the basis of modification 、 Complementary , Adhering to “ Theoretical explanation — case analysis — Application extension ” This feature , Help readers to be more intuitive 、 Learn neural networks vividly .

《MATLAB neural network 43 A case study 》 share 43 Chapter , The content covers common neural networks （BP、RBF、SOM、Hopfield、Elman、LVQ、Kohonen、GRNN、NARX etc. ） And related intelligent algorithms （SVM、 Decision tree 、 Random forests 、 Extreme learning machine, etc ）. meanwhile , Some chapters also cover common optimization algorithms （ Genetic algorithm (ga) 、 Ant colony algorithm, etc ） And neural network . Besides ,《MATLAB neural network 43 A case study 》 It also introduces MATLAB R2012b New functions and features of neural network toolbox in , Such as neural network parallel computing 、 Custom neural networks 、 Efficient programming of neural network, etc .

In recent years, with the rise of artificial intelligence research , The related direction of neural network has also ushered in another upsurge of research , Because of its outstanding performance in the field of signal processing , The neural network method is also being applied to various applications in the direction of speech and image , This paper combines the cases in the book , It is simulated and realized , It's a relearning , I hope I can review the old and know the new , Strengthen and improve my understanding and practice of the application of neural network in various fields . I just started this book on catching more fish , Let's start the simulation example , Mainly to introduce the source code application examples in each chapter , This paper is mainly based on MATLAB2015b(32 position ) Platform simulation implementation , This is the implementation example of customized neural network in Chapter 41 of this book , Don't talk much , Start ！

2. MATLAB Simulation example

open MATLAB, Click on “ Home page ”, Click on “ open ”, Find the sample file

Choose chapter41.m, Click on “ open ”

chapter41.m Source code is as follows ：

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% function ： Implementation of customized neural network - Personalized modeling and Simulation of Neural Networks 
% Environmental Science ：Win7,Matlab2015b
%Modi: C.S
% Time ：2022-06-21
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%% Matlab neural network 43 A case study 

%  Implementation of customized neural network - Personalized modeling and Simulation of Neural Networks 
% by  Wang Xiao Chuan (@ Wang Xiao Chuan _matlab)
% http://www.matlabsky.com
% Email:[email protected]163.com
% http://weibo.com/hgsz2003

%%  Clear environment variables 
clear all
clc
warning off
tic
%%  Build a “ empty ” neural network 
net = network

%%  Input and network layer definition  
net.numInputs = 2;
net.numLayers = 3;

%%  Use view(net) Observe the neural network structure .
view(net)
%  At this time, the neural network has two inputs , Three neuron layers . But please pay attention to ：net.numInputs The settings are 
%  The number of inputs of neural network , The dimension of each input is determined by net.inputs{
    i}.size control .

%%   Threshold connection definition 
net.biasConnect(1) = 1;
net.biasConnect(3) = 1;
%  Or use net.biasConnect = [1; 0; 1];
view(net)

%%  Input and layer connection definitions 
net.inputConnect(1,1) = 1;
net.inputConnect(2,1) = 1;
net.inputConnect(2,2) = 1;
%  Or use net.inputConnect = [1 0; 1 1; 0 0];
view(net)
net.layerConnect = [0 0 0; 0 0 0; 1 1 1];
view(net)
%%  Output connection settings  
net.outputConnect = [0 1 1];
view(net)

%%  Enter Settings 
net.inputs
net.inputs{
    1}
net.inputs{
    1}.processFcns = {
    'removeconstantrows','mapminmax'};
net.inputs{
    2}.size = 5;
net.inputs{
    1}.exampleInput = [0 10 5; 0 3 10];
view(net)

%%  Layer Settings 
net.layers{
    1}
%  Set the number of neurons in the first layer of the neural network to 4 individual , Its transfer function is set to “tansig” and 
%  Set its initialization function to Nguyen-Widrow function .
net.layers{
    1}.size = 4;
net.layers{
    1}.transferFcn = 'tansig';
net.layers{
    1}.initFcn = 'initnw';
%  Set the number of neurons in the second layer to 3 individual , Its transfer function is set to “logsig”, And use “initnw” initialization .
net.layers{
    2}.size = 3;
net.layers{
    2}.transferFcn = 'logsig';
net.layers{
    2}.initFcn = 'initnw';
%  Set the initialization function of the third layer to “initnw”
net.layers{
    3}.initFcn = 'initnw';
view(net)

%%  Output Settings 
net.outputs
net.outputs{
    2}

%%  threshold , Input weight and layer weight settings 
net.biases
net.biases{
    1}
net.inputWeights
net.layerWeights

%%  Set the delay of some weights of the neural network  
net.inputWeights{
    2,1}.delays = [0 1];
net.inputWeights{
    2,2}.delays = 1;
net.layerWeights{
    3,3}.delays = 1;

%%  Network function settings 
%  Set the neural network initialization to “initlay”, In this way, the neural network can follow 
%  The layer initialization function we set “ initnw” namely Nguyen-Widrow To initialize .
net.initFcn = 'initlay';
%  Set the error of neural network to “mse”（mean squared error）, At the same time, the training function of neural network 
%  Set to “trainlm”Levenberg-Marquardt backpropagation).
net.performFcn = 'mse';
net.trainFcn = 'trainlm';
%  In order to make the neural network can randomly divide the training data set , We can divideFcn Set to “dividerand”.
net.divideFcn = 'dividerand';
%  take  plot functions Set to ：“plotperform”,“plottrainstate”
net.plotFcns = {
    'plotperform','plottrainstate'};

%%  Weight threshold size setting 
net.IW{
    1,1}, net.IW{
    2,1}, net.IW{
    2,2}
net.LW{
    3,1}, net.LW{
    3,2}, net.LW{
    3,3}
net.b{
    1}, net.b{
    3}

%%  Neural network initialization 
net = init(net);
net.IW{
    1,1}

%%  Neural network training 
X = {
    [0; 0] [2; 0.5]; [2; -2; 1; 0; 1] [-1; -1; 1; 0; 1]};
T = {
    [1; 1; 1] [0; 0; 0]; 1 -1};
Y = sim(net,X)

%%  Training parameters of neural network 
net.trainParam

%%   Training network 
net = train(net,X,T);

%%  Simulation to check whether the neural network is normal .
Y = sim(net,X)
toc

Add completed , Click on “ function ”, Start emulating , The output simulation results are as follows ：

net =

    Neural Network
 
              name: 'Custom Neural Network'
          userdata: (your custom info)
 
    dimensions:
 
         numInputs: 0
         numLayers: 0
        numOutputs: 0
    numInputDelays: 0
    numLayerDelays: 0
 numFeedbackDelays: 0
 numWeightElements: 0
        sampleTime: 1
 
    connections:
 
       biasConnect: []
      inputConnect: []
      layerConnect: []
     outputConnect: []
 
    subobjects:
 
            inputs: {
    0x1 cell array of 0 inputs}
            layers: {
    0x1 cell array of 0 layers}
           outputs: {
    1x0 cell array of 0 outputs}
            biases: {
    0x1 cell array of 0 biases}
      inputWeights: {
    0x0 cell array of 0 weights}
      layerWeights: {
    0x0 cell array of 0 weights}
 
    functions:
 
          adaptFcn: (none)
        adaptParam: (none)
          derivFcn: 'defaultderiv'
         divideFcn: (none)
       divideParam: (none)
        divideMode: 'sample'
           initFcn: 'initlay'
        performFcn: 'mse'
      performParam: .regularization, .normalization
          plotFcns: {
    }
        plotParams: {
    1x0 cell array of 0 params}
          trainFcn: (none)
        trainParam: (none)
 
    weight and bias values:
 
                IW: {
    0x0 cell} containing 0 input weight matrices
                LW: {
    0x0 cell} containing 0 layer weight matrices
                 b: {
    0x1 cell} containing 0 bias vectors
 
    methods:
 
             adapt: Learn while in continuous use
         configure: Configure inputs & outputs
            gensim: Generate Simulink model
              init: Initialize weights & biases
           perform: Calculate performance
               sim: Evaluate network outputs given inputs
             train: Train network with examples
              view: View diagram
       unconfigure: Unconfigure inputs & outputs
 

ans = 

    [1x1 nnetInput]
    [1x1 nnetInput]


ans = 

    Neural Network Input

              name: 'Input'
    feedbackOutput: []
       processFcns: {
    }
     processParams: {
    1x0 cell array of 0 params}
   processSettings: {
    0x0 cell array of 0 settings}
    processedRange: []
     processedSize: 0
             range: []
              size: 0
          userdata: (your custom info)


ans = 

    Neural Network Layer
 
              name: 'Layer'
        dimensions: 0
       distanceFcn: (none)
     distanceParam: (none)
         distances: []
           initFcn: 'initwb'
       netInputFcn: 'netsum'
     netInputParam: (none)
         positions: []
             range: []
              size: 0
       topologyFcn: (none)
       transferFcn: 'purelin'
     transferParam: (none)
          userdata: (your custom info)
 

ans = 

    []    [1x1 nnetOutput]    [1x1 nnetOutput]


ans = 

    Neural Network Output

              name: 'Output'
     feedbackInput: []
     feedbackDelay: 0
      feedbackMode: 'none'
       processFcns: {
    }
     processParams: {
    1x0 cell array of 0 params}
   processSettings: {
    0x0 cell array of 0 settings}
    processedRange: [3x2 double]
     processedSize: 3
             range: [3x2 double]
              size: 3
          userdata: (your custom info)


ans = 

    [1x1 nnetBias]
    []
    [1x1 nnetBias]


ans = 

    Neural Network Bias

           initFcn: (none)
             learn: true
          learnFcn: (none)
        learnParam: (none)
              size: 4
          userdata: (your custom info)


ans = 

    [1x1 nnetWeight]                  []
    [1x1 nnetWeight]    [1x1 nnetWeight]
                  []                  []


ans = 

                  []                  []                  []
                  []                  []                  []
    [1x1 nnetWeight]    [1x1 nnetWeight]    [1x1 nnetWeight]


ans =

     0     0
     0     0
     0     0
     0     0


ans =

     0     0     0     0
     0     0     0     0
     0     0     0     0


ans =

     0     0     0     0     0
     0     0     0     0     0
     0     0     0     0     0


ans =

    Empty matrix : 0×4


ans =

    Empty matrix : 0×3


ans =

     []


ans =

     0
     0
     0
     0


ans =

    Empty matrix : 0×1


ans =

    1.9468    2.0124
    2.3881   -1.4619
   -1.7285    2.2028
   -0.2749    2.7865


Y = 

    [3x1 double]    [3x1 double]
    [0x1 double]    [0x1 double]


ans = 

 
    Function Parameters for 'trainlm'
 
    Show Training Window Feedback   showWindow: true
    Show Command Line Feedback showCommandLine: false
    Command Line Frequency                show: 25
    Maximum Epochs                      epochs: 1000
    Maximum Training Time                 time: Inf
    Performance Goal                      goal: 0
    Minimum Gradient                  min_grad: 1e-07
    Maximum Validation Checks         max_fail: 6
    Mu                                      mu: 0.001
    Mu Decrease Ratio                   mu_dec: 0.1
    Mu Increase Ratio                   mu_inc: 10
    Maximum mu                          mu_max: 10000000000
 

Y = 

    [3x1 double]    [3x1 double]
    [    1.0000]    [   -1.0000]

 Time has passed  3.932652  second .

In turn, click Plots Medium Performance,Training State The following figure is available ：

3. Summary

This chapter introduces the method of customizing neural network , Including how to set up the network , How to link the network , And through input and output , Connection threshold , Input connection , The connection of output, etc. shall be set accordingly , So as to get the neural network you need , Then import data for corresponding training , After obtaining the model, carry out the prediction test , A complete process of customizing neural network has been realized . Interested in the content of this chapter or want to fully learn and understand , It is suggested to study the contents of Chapter 41 in the book . Some of these knowledge points will be supplemented on the basis of their own understanding in the later stage , Welcome to study and exchange together .