Matlab S-Function模糊神经网络代码

以下是一个简单的Matlab S-Function模糊神经网络代码示例：

function [sys,x0,str,ts] = fuzzy_nn_sfun(t,x,u,flag)

switch flag
    
    case 0 % Initialization
        [sys,x0,str,ts]=mdlInitializeSizes;
        
    case 2 % Update
        sys = mdlUpdate(t,x,u);
        
    case 3 % Outputs
        sys = mdlOutputs(t,x,u);
        
    case {1, 4, 9} % Unused flags
        sys = [];
        
    otherwise % Error handling
        error(['Unhandled flag = ',num2str(flag)]);
        
end

%---------------------------------------------------------------
function [sys,x0,str,ts] = mdlInitializeSizes

sizes = simsizes;
sizes.NumContStates  = 0;
sizes.NumDiscStates  = 0;
sizes.NumOutputs     = 1;
sizes.NumInputs      = 2;
sizes.DirFeedthrough = 1;
sizes.NumSampleTimes = 1;

sys = simsizes(sizes);
x0  = [];
str = [];
ts  = [0 0];

%---------------------------------------------------------------
function sys = mdlUpdate(t,x,u)

% Parameters
alpha = 0.01; % Learning rate
gamma = 0.9;  % Discount factor
nFIS  = 3;    % Number of fuzzy inference systems
nMF   = 3;    % Number of membership functions
nOut  = 1;    % Number of outputs

% Inputs
state = u(1); % State
act   = u(2); % Action

% Fuzzy inference systems
for i = 1:nFIS
    
    % Initialize FIS
    fis = mamfis('NumInputs',2,'NumOutputs',nOut,'NumMFs',nMF);
    
    % Input 1: State
    fis.Inputs(1).Name = 'state';
    fis.Inputs(1).Range = [0 1];
    for j = 1:nMF
        fis.Inputs(1).mf(j).Name = ['state',num2str(j)];
        fis.Inputs(1).mf(j).Type = 'gbellmf';
        fis.Inputs(1).mf(j).Params = [(j-1)/(nMF-1),1,10];
    end
    
    % Input 2: Action
    fis.Inputs(2).Name = 'action';
    fis.Inputs(2).Range = [0 1];
    for j = 1:nMF
        fis.Inputs(2).mf(j).Name = ['action',num2str(j)];
        fis.Inputs(2).mf(j).Type = 'gbellmf';
        fis.Inputs(2).mf(j).Params = [(j-1)/(nMF-1),1,10];
    end
    
    % Output
    fis.Outputs(1).Name = 'q';
    fis.Outputs(1).Range = [-1 1];
    for j = 1:nOut
        fis.Outputs(1).mf(j).Name = ['q',num2str(j)];
        fis.Outputs(1).mf(j).Type = 'linear';
        fis.Outputs(1).mf(j).Params = [1 0];
    end
    
    % Training data
    x = rand(100,2);
    y = rand(100,1);
    
    % Train FIS
    fis = anfis([x y],fis,[100 0 0.01 0.9]);
    
end

% Output
sys = [];

%---------------------------------------------------------------
function sys = mdlOutputs(t,x,u)

% Parameters
alpha = 0.01; % Learning rate
gamma = 0.9;  % Discount factor
nFIS  = 3;    % Number of fuzzy inference systems
nMF   = 3;    % Number of membership functions
nOut  = 1;    % Number of outputs

% Inputs
state = u(1); % State
act   = u(2); % Action

% Fuzzy inference systems
for i = 1:nFIS
    
    % Initialize FIS
    fis = mamfis('NumInputs',2,'NumOutputs',nOut,'NumMFs',nMF);
    
    % Input 1: State
    fis.Inputs(1).Name = 'state';
    fis.Inputs(1).Range = [0 1];
    for j = 1:nMF
        fis.Inputs(1).mf(j).Name = ['state',num2str(j)];
        fis.Inputs(1).mf(j).Type = 'gbellmf';
        fis.Inputs(1).mf(j).Params = [(j-1)/(nMF-1),1,10];
    end
    
    % Input 2: Action
    fis.Inputs(2).Name = 'action';
    fis.Inputs(2).Range = [0 1];
    for j = 1:nMF
        fis.Inputs(2).mf(j).Name = ['action',num2str(j)];
        fis.Inputs(2).mf(j).Type = 'gbellmf';
        fis.Inputs(2).mf(j).Params = [(j-1)/(nMF-1),1,10];
    end
    
    % Output
    fis.Outputs(1).Name = 'q';
    fis.Outputs(1).Range = [-1 1];
    for j = 1:nOut
        fis.Outputs(1).mf(j).Name = ['q',num2str(j)];
        fis.Outputs(1).mf(j).Type = 'linear';
        fis.Outputs(1).mf(j).Params = [1 0];
    end
    
    % Training data
    x = rand(100,2);
    y = rand(100,1);
    
    % Train FIS
    fis = anfis([x y],fis,[100 0 0.01 0.9]);
    
    % Evaluate FIS
    q = evalfis([state act],fis);
    
    % Update Q-values
    % ...
    
end

% Output
sys = q;

请注意，此代码示例仅用于说明如何在Matlab中使用S-Function实现模糊神经网络。您需要根据自己的需求进行修改和定制。