%% 划分训练集和测试集

M = size(P_train, 2);

N = size(P_test, 2);

or_dim = size(P_train,1) ; % 记录特征数据维度

n_out = 1 ; % % 预测步长

% 数据归一化

[p_train, ps_input] = mapminmax(P_train, 0, 1);

p_test = mapminmax('apply', P_test, ps_input);

[t_train, ps_output] = mapminmax(T_train, 0, 1);

t_test = mapminmax('apply', T_test, ps_output);

%% 数据平铺

for i = 1:size(p_train,2)

trainD{i,:} = (reshape(p_train(:,i),or_dim,[]));

end

for i = 1:size(p_test,2)

testD{i,:} = (reshape(p_test(:,i),or_dim,[]));

end

targetD = t_train';

targetD_test = t_test';

%% 优化算法参数设置

%参数取值上界(学习率，隐藏层节点，正则化系数)

fitness = @fical;

%% 贝叶斯优化参数范围

optimVars = [

optimizableVariable('NumOfUnits', [10, 50], 'Type', 'integer')

optimizableVariable('InitialLearnRate', [1e-3, 1], 'Transform', 'log')

optimizableVariable('L2Regularization', [1e-10, 1e-2], 'Transform', 'log')];

智能算法及其模型预测