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改进的BP神经网络算法用于预测温度值的变化

backend 2025/8/11 0:45:27

基于MATLAB实现的改进BP神经网络算法用于预测温度值变化的代码，结合了遗传算法（GA）优化BP神经网络的权值和阈值，以提高预测精度。

1. 定义BP神经网络结构

% 定义BP神经网络结构
inputLayerSize = 7; % 输入层节点数
hiddenLayerSize = 7; % 隐藏层节点数
outputLayerSize = 1; % 输出层节点数

2. 初始化BP神经网络

% 初始化BP神经网络
net = feedforwardnet(hiddenLayerSize);
net.numInputs = 1;
net.numLayers = 2;
net.layers{1}.size = hiddenLayerSize;
net.layers{2}.size = outputLayerSize;
net.layers{1}.transferFcn = 'logsig';
net.layers{2}.transferFcn = 'purelin';
net.trainFcn = 'trainlm'; % Levenberg-Marquardt算法
net.performFcn = 'mse'; % 均方误差

3. 遗传算法优化BP神经网络

% 遗传算法优化BP神经网络
options = optimoptions('ga', 'PopulationSize', 100, 'MaxGenerations', 100, 'Display', 'iter');
[x, fval] = ga(@(x) trainAndTestBP(x, net, inputLayerSize, hiddenLayerSize, outputLayerSize, trainingData, validationData), ...inputLayerSize * hiddenLayerSize + hiddenLayerSize * outputLayerSize + hiddenLayerSize + outputLayerSize, [], [], [], [], [], [], [], options);% 更新BP神经网络的权值和阈值
net.IW{1,1} = reshape(x(1:inputLayerSize*hiddenLayerSize), [inputLayerSize, hiddenLayerSize]);
net.LW{2,1} = reshape(x(inputLayerSize*hiddenLayerSize+1:end-outputLayerSize-outputLayerSize), [hiddenLayerSize, outputLayerSize]);
net.b{1} = x(end-outputLayerSize-outputLayerSize+1:end-outputLayerSize);
net.b{2} = x(end-outputLayerSize+1:end);

4. 训练和测试BP神经网络

% 训练和测试BP神经网络
function mse = trainAndTestBP(x, net, inputLayerSize, hiddenLayerSize, outputLayerSize, trainingData, validationData)% 更新BP神经网络的权值和阈值net.IW{1,1} = reshape(x(1:inputLayerSize*hiddenLayerSize), [inputLayerSize, hiddenLayerSize]);net.LW{2,1} = reshape(x(inputLayerSize*hiddenLayerSize+1:end-outputLayerSize-outputLayerSize), [hiddenLayerSize, outputLayerSize]);net.b{1} = x(end-outputLayerSize-outputLayerSize+1:end-outputLayerSize);net.b{2} = x(end-outputLayerSize+1:end);% 训练BP神经网络[net, tr] = train(net, trainingData.inputs, trainingData.targets);% 测试BP神经网络outputs = net(validationData.inputs);mse = perform(net, validationData.targets, outputs);
end

5. 准备训练和验证数据

% 准备训练和验证数据
% 假设trainingData和validationData是结构体，包含输入和目标数据
% trainingData.inputs: 训练输入数据
% trainingData.targets: 训练目标数据
% validationData.inputs: 验证输入数据
% validationData.targets: 验证目标数据
% 这里需要根据实际数据进行调整

6. 运行改进的BP神经网络

% 运行改进的BP神经网络
% 假设trainingData和validationData已经准备好
% 调用遗传算法优化BP神经网络
[x, fval] = ga(@(x) trainAndTestBP(x, net, inputLayerSize, hiddenLayerSize, outputLayerSize, trainingData, validationData), ...inputLayerSize * hiddenLayerSize + hiddenLayerSize * outputLayerSize + hiddenLayerSize + outputLayerSize, [], [], [], [], [], [], [], options);% 更新BP神经网络的权值和阈值
net.IW{1,1} = reshape(x(1:inputLayerSize*hiddenLayerSize), [inputLayerSize, hiddenLayerSize]);
net.LW{2,1} = reshape(x(inputLayerSize*hiddenLayerSize+1:end-outputLayerSize-outputLayerSize), [hiddenLayerSize, outputLayerSize]);
net.b{1} = x(end-outputLayerSize-outputLayerSize+1:end-outputLayerSize);
net.b{2} = x(end-outputLayerSize+1:end);% 测试改进的BP神经网络
outputs = net(validationData.inputs);
mse = perform(net, validationData.targets, outputs);
disp(['Mean Squared Error: ', num2str(mse)]);