matlab神经网络工具箱的使用

本是古典何须时尚 2022-10-24 15:22 238阅读 0赞

单变量  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70]  
单变量取数据

data = load('ex1data1.txt');
    X = data(:, 1); y = data(:, 2);

多变量取数据

data = load('ex1data2.txt');
    X = data(:, 1:2);
    y = data(:, 3);

![在这里插入图片描述][2021013008380494.png]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 3]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 4]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 5]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 6]  
运行train后弹出  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 7]  
对应的图![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 8]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L1NoYXJwZW5fXw_size_16_color_FFFFFF_t_70 9]  
比如simulink格式  
![在这里插入图片描述][2021013009005173.png]  
graphics格式  
![在这里插入图片描述][20210130090132972.png]  
导出的函数形式

function [Y,Xf,Af] = myNeuralNetworkFunction(X,~,~)
    %MYNEURALNETWORKFUNCTION neural network simulation function.
    %
    % Generated by Neural Network Toolbox function genFunction, 30-Jan-2021 09:01:36.
    %
    % [Y] = myNeuralNetworkFunction(X,~,~) takes these arguments:
    %
    %   X = 1xTS cell, 1 inputs over TS timesteps
    %   Each X{
        1,ts} = Qx1 matrix, input #1 at timestep ts.
    %
    % and returns:
    %   Y = 1xTS cell of 1 outputs over TS timesteps.
    %   Each Y{
        1,ts} = Qx1 matrix, output #1 at timestep ts.
    %
    % where Q is number of samples (or series) and TS is the number of timesteps.
    
    %#ok<*RPMT0>
    
    % ===== NEURAL NETWORK CONSTANTS =====
    
    % Input 1
    x1_step1.xoffset = 5.0269;
    x1_step1.gain = 0.116440868415997;
    x1_step1.ymin = -1;
    
    % Layer 1
    b1 = [14.042816227217203;10.829822894133768;-7.7236816033501636;4.6630989655237869;-1.6040654273298633;-0.65993667173065473;4.2289667275540159;-9.1592130579667526;12.13654091898813;-15.703978850857604];
    IW1_1 = [-13.961737650057232;-14.033143496877679;14.033592553636298;-14.000368259946766;13.989659274275441;-13.927198867729832;11.381031227414059;-18.718887023153201;15.767556263365563;-12.280677436660298];
    
    % Layer 2
    b2 = 1.0050712872879728;
    LW2_1 = [-0.25740959435228478 0.25630456816917047 0.41476156959461874 -0.095876155369502725 -0.24659666733636154 -0.35777296228019673 0.095335365316523368 -0.01948709416037836 -0.044091570515430667 0.69616590295248804];
    
    % Output 1
    y1_step1.ymin = -1;
    y1_step1.gain = 0.0745498123208475;
    y1_step1.xoffset = -2.6807;
    
    % ===== SIMULATION ========
    
    % Format Input Arguments
    isCellX = iscell(X);
    if ~isCellX, X = {
        X}; end;
    
    % Dimensions
    TS = size(X,2); % timesteps
    if ~isempty(X)
        Q = size(X{
        1},1); % samples/series
    else
        Q = 0;
    end
    
    % Allocate Outputs
    Y = cell(1,TS);
    
    % Time loop
    for ts=1:TS
        
        % Input 1
        X{
        1,ts} = X{
        1,ts}';
        Xp1 = mapminmax_apply(X{1,ts},x1_step1);
        
        % Layer 1
        a1 = tansig_apply(repmat(b1,1,Q) + IW1_1*Xp1);
        
        % Layer 2
        a2 = repmat(b2,1,Q) + LW2_1*a1;
        
        % Output 1
        Y{1,ts} = mapminmax_reverse(a2,y1_step1);
        Y{1,ts} = Y{1,ts}';
    end
    
    % Final Delay States
    Xf = cell(1,0);
    Af = cell(2,0);
    
    % Format Output Arguments
    if ~isCellX, Y = cell2mat(Y); end
    end
    
    % ===== MODULE FUNCTIONS ========
    
    % Map Minimum and Maximum Input Processing Function
    function y = mapminmax_apply(x,settings)
    y = bsxfun(@minus,x,settings.xoffset);
    y = bsxfun(@times,y,settings.gain);
    y = bsxfun(@plus,y,settings.ymin);
    end
    
    % Sigmoid Symmetric Transfer Function
    function a = tansig_apply(n,~)
    a = 2 ./ (1 + exp(-2*n)) - 1;
    end
    
    % Map Minimum and Maximum Output Reverse-Processing Function
    function x = mapminmax_reverse(y,settings)
    x = bsxfun(@minus,y,settings.ymin);
    x = bsxfun(@rdivide,x,settings.gain);
    x = bsxfun(@plus,x,settings.xoffset);
    end

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[2021013008380494.png]: /images/20221024/80d8141e09b04f4da186d31f55b95455.png
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[2021013009005173.png]: /images/20221024/8045eb05fc1e4eb289086bcb3d325422.png
[20210130090132972.png]: /images/20221024/871f5381c8eb4484b717f9ef05b24ab4.png