遗传算法优化bp神经网络matlab代码

以下是基于遗传算法优化BP神经网络的MATLAB代码示例：

% 数据准备
load iris_dataset.mat
inputs = irisInputs;
targets = irisTargets;

% 神经网络结构
inputSize = 4;
hiddenSize = 5;
outputSize = 3;
net = feedforwardnet(hiddenSize);
net.layers{1}.transferFcn = 'logsig';
net.layers{2}.transferFcn = 'logsig';
net.numInputs = inputSize;
net.numLayers = 2;
net.numOutputs = outputSize;

% 遗传算法参数设置
popSize = 20; % 种群大小
numGen = 50; % 迭代次数
mutationRate = 0.01; % 变异率
crossoverRate = 0.8; % 交叉率

% 遗传算法优化
fitnessFunction = @(x) bpnnFitnessFunction(x, inputs, targets, net);
options = gaoptimset('PopulationSize', popSize, 'Generations', numGen, 'CrossoverFraction', crossoverRate, 'MutationFcn', {@mutationuniform, mutationRate}, 'Display', 'iter');
[bestWeights, bestFitness] = ga(fitnessFunction, inputSize * hiddenSize + hiddenSize * outputSize, options);

% 使用优化后的权重更新神经网络
net = setwb(net, bestWeights');
outputs = net(inputs);

% 计算分类准确率
[~, predictedTargets] = max(outputs);
[~, targets] = max(targets);
accuracy = sum(predictedTargets == targets) / length(targets)

function fitness = bpnnFitnessFunction(weights, inputs, targets, net)
    % 将权重向量转换为网络中的权重矩阵
    net = setwb(net, weights');
    
    % 计算神经网络输出
    outputs = net(inputs);
    
    % 计算分类误差
    [~, predictedTargets] = max(outputs);
    [~, targets] = max(targets);
    classError = sum(predictedTargets ~= targets) / length(targets);
    
    % 计算适应度
    fitness = 1 - classError;
end

其中，`bpnnFitnessFunction` 函数计算每组权重的适应度，其输入参数为权重向量、输入数据、目标数据和神经网络对象；输出参数为适应度值。`ga` 函数使用遗传算法优化权重矩阵，并返回最优解和最优适应度。最后，使用最优解更新神经网络，计算分类准确率。