GA-BPNN matlab代码

以下是GA-BPNN的MATLAB代码示例：

% GA-BPNN算法
% 数据预处理
x = input_data; % 输入数据
y = output_data; % 输出数据
x = x'; % 转置
y = y'; % 转置
p = size(x, 2); % 输入数据的维数
q = size(y, 2); % 输出数据的维数
n = 10; % 隐层神经元个数
m = 100; % 最大迭代次数
err = 1e-5; % 最小误差
pop_size = 50; % 种群大小
chrom_len = (p + 1) * n + (n + 1) * q; % 染色体长度
pc = 0.6; % 交叉概率
pm = 0.01; % 变异概率
% 初始化种群
pop = rand(pop_size, chrom_len);
% 计算适应度值
for k = 1 : pop_size
    w1 = reshape(pop(k, 1 : (p + 1) * n), p + 1, n);
    w2 = reshape(pop(k, (p + 1) * n + 1 : chrom_len), n + 1, q);
    y_hat = bpnn(x, w1, w2);
    err = sum(sum(abs(y_hat - y))) / numel(y);
    fitness(k) = 1 / err;
end
% 进化迭代
for i = 1 : m
    % 选择操作
    [val, idx] = sort(fitness, 'descend');
    parents = pop(idx(1 : pop_size * 0.2), :);
    % 交叉操作
    for j = 1 : pop_size * 0.2
        if rand(1) < pc
            p1 = parents(j, :);
            p2 = parents(randi(pop_size * 0.2), :);
            c = crossover(p1, p2);
            children(j, :) = c;
        else
            children(j, :) = parents(j, :);
        end
    end
    % 变异操作
    for j = 1 : pop_size * 0.2
        if rand(1) < pm
            children(j, :) = mutation(children(j, :));
        end
    end
    % 合并操作
    pop = [parents; children];
    % 计算适应度值
    for k = 1 : pop_size
        w1 = reshape(pop(k, 1 : (p + 1) * n), p + 1, n);
        w2 = reshape(pop(k, (p + 1) * n + 1 : chrom_len), n + 1, q);
        y_hat = bpnn(x, w1, w2);
        err = sum(sum(abs(y_hat - y))) / numel(y);
        fitness(k) = 1 / err;
    end
    % 判断是否结束
    if max(fitness) > 1 / err
        w1 = reshape(pop(k, 1 : (p + 1) * n), p + 1, n);
        w2 = reshape(pop(k, (p + 1) * n + 1 : chrom_len), n + 1, q);
        break;
    end
end
% 测试数据
x_test = input_test;
y_test = output_test;
x_test = x_test';
y_test = y_test';
y_hat_test = bpnn(x_test, w1, w2);
err_test = sum(sum(abs(y_hat_test - y_test))) / numel(y_test);

其中，`bpnn`为BP神经网络的实现函数，`crossover`为交叉操作的实现函数，`mutation`为变异操作的实现函数。需要根据具体的问题进行相应的修改。