狼优化bp,粒子群优化bp,鲸鱼优化bp放在一起比较的matlab代码

以下是一个简单的Matlab代码，用于演示如何将灰狼优化、粒子群优化和鲸鱼优化算法与BP算法结合起来。这个代码仅是一个示例，您可能需要根据您自己的数据和问题进行修改和调整。

% 加载数据集
load iris_dataset.mat

% 将数据集分成训练集和测试集
train_ratio = 0.7;
[train_data, train_label, test_data, test_label] = split_dataset(data, labels, train_ratio);

% 初始化BP神经网络
input_size = size(train_data, 2);
hidden_size = 10;
output_size = size(unique(train_label), 1);
net = init_bp_network(input_size, hidden_size, output_size);

% 设置灰狼优化算法的参数
wo_iter = 100;
wo_pop_size = 20;
wo_lb = -1;
wo_ub = 1;

% 设置粒子群优化算法的参数
pso_iter = 100;
pso_pop_size = 20;
pso_w = 0.8;
pso_c1 = 1.5;
pso_c2 = 1.5;
pso_lb = -1;
pso_ub = 1;

% 设置鲸鱼优化算法的参数
wo_iter = 100;
wo_pop_size = 20;
wo_a = 0.5;
wo_A = 2;
wo_lb = -1;
wo_ub = 1;

% 初始化权值和阈值矩阵
weights = init_weights(input_size, hidden_size, output_size);
biases = init_biases(hidden_size, output_size);

% 将权值和阈值矩阵展开成一维向量
params = [weights(:); biases(:)];

% 使用灰狼优化算法优化权值和阈值矩阵
wo_params = wo_train_bp(train_data, train_label, net, wo_iter, wo_pop_size, wo_lb, wo_ub);

% 使用粒子群优化算法优化权值和阈值矩阵
pso_params = pso_train_bp(train_data, train_label, net, pso_iter, pso_pop_size, pso_w, pso_c1, pso_c2, pso_lb, pso_ub);

% 使用鲸鱼优化算法优化权值和阈值矩阵
wo_params = wo_train_bp(train_data, train_label, net, wo_iter, wo_pop_size, wo_lb, wo_ub);

% 计算灰狼优化算法的预测准确率
wo_weights = reshape(wo_params(1:input_size*hidden_size), input_size, hidden_size);
wo_biases = reshape(wo_params(input_size*hidden_size+1:end), 1, []);
wo_net = set_bp_network(net, wo_weights, wo_biases);
wo_pred = bp_predict(test_data, wo_net);
wo_acc = accuracy(wo_pred, test_label);

% 计算粒子群优化算法的预测准确率
pso_weights = reshape(pso_params(1:input_size*hidden_size), input_size, hidden_size);
pso_biases = reshape(pso_params(input_size*hidden_size+1:end), 1, []);
pso_net = set_bp_network(net, pso_weights, pso_biases);
pso_pred = bp_predict(test_data, pso_net);
pso_acc = accuracy(pso_pred, test_label);

% 计算鲸鱼优化算法的预测准确率
wo_weights = reshape(wo_params(1:input_size*hidden_size), input_size, hidden_size);
wo_biases = reshape(wo_params(input_size*hidden_size+1:end), 1, []);
wo_net = set_bp_network(net, wo_weights, wo_biases);
wo_pred = bp_predict(test_data, wo_net);
wo_acc = accuracy(wo_pred, test_label);

% 显示预测准确率
disp(['灰狼优化算法的预测准确率：' num2str(wo_acc)]);
disp(['粒子群优化算法的预测准确率：' num2str(pso_acc)]);
disp(['鲸鱼优化算法的预测准确率：' num2str(wo_acc)]);

需要注意的是，上述代码中的`init_bp_network`、`init_weights`、`init_biases`、`set_bp_network`、`bp_predict`和`accuracy`等函数需要根据您的具体情况进行编写或调用。此外，灰狼优化、粒子群优化和鲸鱼优化算法的训练函数`wo_train_bp`、`pso_train_bp`和`wo_train_bp`也需要您自己编写或调用。