帮我写一个基于循环神经网络的多输出回归预测模型的MATLAB代码
时间: 2023-08-28 08:39:10 浏览: 104
当然可以!以下是一个基于循环神经网络的多输出回归预测模型的MATLAB代码,希望能够帮到你:
```matlab
% 加载数据集
load data.mat
% 将数据集分为训练集和测试集
train_data = data(:, 1:end-1);
train_label = data(:, end);
train_ratio = 0.8;
train_size = round(size(train_data, 1) * train_ratio);
train_data = train_data(1:train_size, :);
train_label = train_label(1:train_size, :);
test_data = train_data(train_size+1:end, :);
test_label = train_label(train_size+1:end, :);
% 设置模型参数
input_size = size(train_data, 2);
output_size = size(train_label, 2);
hidden_size = 10;
learning_rate = 0.01;
max_epoch = 100;
% 初始化权重和偏置
Wxh = randn(hidden_size, input_size) * 0.01;
Whh = randn(hidden_size, hidden_size) * 0.01;
Why = randn(output_size, hidden_size) * 0.01;
bh = zeros(hidden_size, 1);
by = zeros(output_size, 1);
% 训练模型
for epoch = 1:max_epoch
for i = 1:size(train_data, 1)
% 前向传播
h = zeros(hidden_size, 1);
for t = 1:input_size
x = train_data(i, t)';
h = tanh(Wxh*x + Whh*h + bh);
end
y = Why*h + by;
% 反向传播
dWhy = (y - train_label(i, :)')*h';
dby = (y - train_label(i, :)');
dh_next = zeros(hidden_size, 1);
for t = input_size:-1:1
x = train_data(i, t)';
h_prev = zeros(hidden_size, 1);
if t > 1
h_prev = h(:, t-1);
end
[dh, dWxh, dWhh, dbh] = rnn_backward(dh_next, h_prev, x, h, Wxh, Whh, bh);
dh_next = dh;
Wxh = Wxh - learning_rate*dWxh;
Whh = Whh - learning_rate*dWhh;
bh = bh - learning_rate*dbh;
end
Why = Why - learning_rate*dWhy;
by = by - learning_rate*dby;
end
% 测试模型
loss = 0;
for i = 1:size(test_data, 1)
h = zeros(hidden_size, 1);
for t = 1:input_size
x = test_data(i, t)';
h = tanh(Wxh*x + Whh*h + bh);
end
y = Why*h + by;
loss = loss + norm(y - test_label(i, :)');
end
fprintf('Epoch %d, loss = %f\n', epoch, loss/size(test_data, 1));
end
% 循环神经网络反向传播函数
function [dh, dWxh, dWhh, dbh] = rnn_backward(dh_next, h_prev, x, h, Wxh, Whh, bh)
dz = dh_next.*(1 - h.*h);
dh = Whh'*dz;
dh = dh + h_prev.*(1 - h_prev.*h_prev).*Whh'*dz;
dWxh = dz*x';
dWhh = dz*h_prev';
dbh = dz;
end
```
这个模型使用循环神经网络来实现多输出回归预测,使用tanh作为激活函数,采用随机梯度下降法来优化模型。在训练模型时,我们使用了训练数据集进行训练,并使用测试数据集进行测试。如果有任何疑问,请随时问我!