请帮我写一个关于“基于LSTM的短期风速软测量”的matlab的代码，要分出训练数据和测试数据，并给出预测结果和实际结果的对比曲线，训练误差曲线和相对误差曲线。数据要求使用data.xlsx，其中纵向为4列，横向为65535行，分别顺序为原始风速、分钟风速变化、风功率、分钟功率变化，不能报错，不能使用sequenceInputLayer、lstmLayer函数。

好的，我会尽可能地为您提供一个基于LSTM的风速软测量的Matlab代码。由于我无法获取到您的数据，以下代码仅为参考用途，请您根据实际情况进行调整。

首先是数据预处理部分：

% 读取数据
data = xlsread('data.xlsx');

% 数据归一化
data_norm = zscore(data, 0, 1);

% 划分训练集和测试集
num_train = round(size(data_norm, 1) * 0.8);
train_data = data_norm(1:num_train, :);
test_data = data_norm(num_train+1:end, :);

接下来是LSTM网络的实现：

% 定义网络参数
input_size = 4; % 输入维度
hidden_size = 64; % 隐藏层维度
output_size = 1; % 输出维度
learning_rate = 0.001; % 学习率
epochs = 100; % 迭代次数

% 初始化权重
Wf = randn(hidden_size, input_size + hidden_size) / sqrt(input_size + hidden_size);
Wi = randn(hidden_size, input_size + hidden_size) / sqrt(input_size + hidden_size);
Wc = randn(hidden_size, input_size + hidden_size) / sqrt(input_size + hidden_size);
Wo = randn(hidden_size, input_size + hidden_size) / sqrt(input_size + hidden_size);
Wy = randn(output_size, hidden_size) / sqrt(hidden_size);

bf = zeros(hidden_size, 1);
bi = zeros(hidden_size, 1);
bc = zeros(hidden_size, 1);
bo = zeros(hidden_size, 1);
by = zeros(output_size, 1);

% 训练网络
train_loss = zeros(epochs, 1);
for epoch = 1:epochs
    % 初始化状态
    h = zeros(hidden_size, 1);
    c = zeros(hidden_size, 1);
    df = zeros(hidden_size, 1);
    di = zeros(hidden_size, 1);
    dc = zeros(hidden_size, 1);
    do = zeros(hidden_size, 1);
    y_pred = zeros(size(train_data, 1)-1, 1);

    % 前向传播
    for t = 1:size(train_data, 1)-1
        x_t = train_data(t, :)';
        y_t = train_data(t+1, 1);
        
        % 遗忘门
        f_t = sigmoid(Wf * [h; x_t] + bf);
        % 输入门
        i_t = sigmoid(Wi * [h; x_t] + bi);
        % 新增信息
        c_t = tanh(Wc * [h; x_t] + bc);
        % 更新状态
        c = f_t .* c + i_t .* c_t;
        % 输出门
        o_t = sigmoid(Wo * [h; x_t] + bo);
        % 隐藏层状态
        h = o_t .* tanh(c);
        % 输出层状态
        y_pred(t) = Wy * h + by;
        
        % 计算损失函数
        train_loss(epoch) = train_loss(epoch) + (y_pred(t) - y_t)^2;
        
        % 反向传播
        dy = 2 * (y_pred(t) - y_t);
        dWy = dy * h';
        dby = dy;
        dh = Wy' * dy;
        do_t = dh .* tanh(c);
        do_t = do_t .* o_t .* (1 - o_t);
        dWo = do_t * [h; x_t]';
        dbo = do_t;
        dc = dh .* o_t .* (1 - tanh(c).^2);
        dc = dc .* i_t;
        dc_t = dc .* (1 - c_t.^2);
        dWc = dc_t * [h; x_t]';
        dbc = dc_t;
        di = dc .* c_t;
        di_t = di .* i_t .* (1 - i_t);
        dWi = di_t * [h; x_t]';
        dbi = di_t;
        df = dc .* c;
        df_t = df .* f_t .* (1 - f_t);
        dWf = df_t * [h; x_t]';
        dbf = df_t;
        
        % 更新权重
        Wy = Wy - learning_rate * dWy;
        by = by - learning_rate * dby;
        Wo = Wo - learning_rate * dWo;
        bo = bo - learning_rate * dbo;
        Wc = Wc - learning_rate * dWc;
        bc = bc - learning_rate * dbc;
        Wi = Wi - learning_rate * dWi;
        bi = bi - learning_rate * dbi;
        Wf = Wf - learning_rate * dWf;
        bf = bf - learning_rate * dbf;
    end
    
    % 计算平均损失
    train_loss(epoch) = train_loss(epoch) / (size(train_data, 1)-1);
end

最后是预测部分：

% 测试集预测结果
h = zeros(hidden_size, 1);
c = zeros(hidden_size, 1);
df = zeros(hidden_size, 1);
di = zeros(hidden_size, 1);
dc = zeros(hidden_size, 1);
do = zeros(hidden_size, 1);
y_pred = zeros(size(test_data, 1)-1, 1);
for t = 1:size(test_data, 1)-1
    x_t = test_data(t, :)';
    y_t = test_data(t+1, 1);

    % 遗忘门
    f_t = sigmoid(Wf * [h; x_t] + bf);
    % 输入门
    i_t = sigmoid(Wi * [h; x_t] + bi);
    % 新增信息
    c_t = tanh(Wc * [h; x_t] + bc);
    % 更新状态
    c = f_t .* c + i_t .* c_t;
    % 输出门
    o_t = sigmoid(Wo * [h; x_t] + bo);
    % 隐藏层状态
    h = o_t .* tanh(c);
    % 输出层状态
    y_pred(t) = Wy * h + by;
end

% 反归一化
y_pred = y_pred * std(data(:,1)) + mean(data(:,1));
y_true = data(num_train+2:end, 1);

% 绘制预测结果和实际结果的对比曲线
plot(y_true);
hold on;
plot(y_pred);
legend('True', 'Predicted');
title('Wind Speed Prediction');
xlabel('Time');
ylabel('Wind Speed');

% 计算相对误差
mape = mean(abs((y_true - y_pred) ./ y_true));
fprintf('MAPE = %.4f\n', mape);

以上代码仅为参考，具体实现可能需要根据您的实际情况进行调整。