粒子群算法优化BP方法

function pso_bpnn()

% 网络参数

inputSize = 10; % 输入层节点数

hiddenSize = 5; % 隐藏层节点数

outputSize = 1; % 输出层节点数

% 粒子群参数

nParticles = 30; % 粒子数量

maxIter = 100; % 最大迭代次数

c1 = 2; % 个体学习因子

c2 = 2; % 社会学习因子

w = 0.8; % 惯性权重

% 初始化粒子群

positions = rand(nParticles, (inputSize+1)*hiddenSize + (hiddenSize+1)*outputSize);

velocities = zeros(nParticles, size(positions, 2));

pBest = positions;

pBestScores = inf(size(positions, 1), 1);

[gBest, ~] = min(pBestScores);

gBestPosition = positions(gBest, :);

% 主循环

for iter = 1:maxIter

for i = 1:nParticles

% 更新速度和位置

velocities(i, :) = w * velocities(i, :) ...

+ c1 * rand * (pBest(i, :) - positions(i, :)) ...

+ c2 * rand * (gBestPosition - positions(i, :));

positions(i, :) = positions(i, :) + velocities(i, :);

% 评估粒子

fitnessScore = evaluateFitness(positions(i, :), inputSize, hiddenSize, outputSize);

% 更新个体最优

if fitnessScore < pBestScores(i)

pBestScores(i) = fitnessScore;

pBest(i, :) = positions(i, :);

end

% 更新全局最优

if fitnessScore < pBestScores(gBest)

gBest = i;

gBestPosition = positions(i, :);

end

end

% 打印进度

fprintf('Iteration %d: Best Score = %f\n', iter, pBestScores(gBest));

end

% 输出最优解

fprintf('Optimal weights found:\n');

disp(gBestPosition);

end

function score = evaluateFitness(weights, inputSize, hiddenSize, outputSize)

% 划分权重

W1 = reshape(weights(1:(inputSize+1)*hiddenSize), hiddenSize, inputSize+1);

b1 = W1(:, end);

W1 = W1(:, 1:end-1);

W2 = reshape(weights((inputSize+1)*hiddenSize+1:end), outputSize, hiddenSize+1);

b2 = W2(:, end);

W2 = W2(:, 1:end-1);

% 假设有一些训练数据和标签

% 这里需要替换为你的数据

inputs = rand(inputSize, 100); % 输入数据

targets = rand(outputSize, 100); % 目标数据

% 前向传播

z2 = W1 * [inputs; ones(1, size(inputs, 2))] + b1;

a2 = tanh(z2);

z3 = W2 * [a2; ones(1, size(a2, 2))] + b2;

outputs = sigmoid(z3);

% 计算误差

error = outputs - targets;

score = mean(error.^2); % 均方误差

end

function s = sigmoid(x)

s = 1 ./ (1 + exp(-x));

end