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%% 记录最佳参数
Best_pos(1, 2) round(Best_pos(1, 2));
best_lr Best_pos(1, 1);
best_hd Best_pos(1, 2);
best_l2 Best_pos(1, 3);%% 建立模型
% ---------------------- 修改模型结构时需对应修改fical.m中的模型结构 --------------------------
layers [sequenceInputLayer(f_) % 输入层fullyConnectedLayer(outdim) % 输出回归层regressionLayer];%% 参数设置
% ---------------------- 修改模型参数时需对应修改fical.m中的模型参数 --------------------------
options trainingOptions(adam, ... % Adam 梯度下降算法MaxEpochs, 500, ... % 最大训练次数 500InitialLearnRate, best_lr, ... % 初始学习率 best_lrLearnRateSchedule, piecewise, ... % 学习率下降LearnRateDropFactor, 0.5, ... % 学习率下降因子 0.1LearnRateDropPeriod, 400, ... % 经过 400 次训练后 学习率为 best_lr * 0.5Shuffle, every-epoch, ... % 每次训练打乱数据集ValidationPatience, Inf, ... % 关闭验证L2Regularization, best_l2, ... % 正则化参数Plots, training-progress, ... % 画出曲线Verbose, false);%% 训练模型
net trainNetwork(p_train, t_train, layers, options);%% 仿真验证
t_sim1 predict(net, p_train);
t_sim2 predict(net, p_test );%% 数据反归一化
T_sim1 mapminmax(reverse, t_sim1, ps_output);
T_sim2 mapminmax(reverse, t_sim2, ps_output);
T_sim1double(T_sim1);
T_sim2double(T_sim2);
%% 均方根误差
error1 sqrt(sum((T_sim1 - T_train).^2) ./ M);
error2 sqrt(sum((T_sim2 - T_test ).^2) ./ N);
%_________________________________________________________________________%
% The Whale Optimization Algorithm
function [Best_Cost,Best_pos,curve]WOA(pop,Max_iter,lb,ub,dim,fobj)% initialize position vector and score for the leader
Best_poszeros(1,dim);
Best_Costinf; %change this to -inf for maximization problems%Initialize the positions of search agents
Positionsinitialization(pop,dim,ub,lb);curvezeros(1,Max_iter);t0;% Loop counter% Main loop
while tMax_iterfor i1:size(Positions,1)% Return back the search agents that go beyond the boundaries of the search spaceFlag4ubPositions(i,:)ub;Flag4lbPositions(i,:)lb;Positions(i,:)(Positions(i,:).*(~(Flag4ubFlag4lb)))ub.*Flag4ublb.*Flag4lb;% Calculate objective function for each search agentfitnessfobj(Positions(i,:));% Update the leaderif fitnessBest_Cost % Change this to for maximization problemBest_Costfitness; % Update alphaBest_posPositions(i,:);endenda2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)a2-1t*((-1)/Max_iter);% Update the Position of search agents for i1:size(Positions,1)r1rand(); % r1 is a random number in [0,1]r2rand(); % r2 is a random number in [0,1]A2*a*r1-a; % Eq. (2.3) in the paperC2*r2; % Eq. (2.4) in the paperb1; % parameters in Eq. (2.5)l(a2-1)*rand1; % parameters in Eq. (2.5)p rand(); % p in Eq. (2.6)for j1:size(Positions,2)if p0.5 if abs(A)1rand_leader_index floor(pop*rand()1);X_rand Positions(rand_leader_index, :);D_X_randabs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)Positions(i,j)X_rand(j)-A*D_X_rand; % Eq. (2.8)elseif abs(A)1D_Leaderabs(C*Best_pos(j)-Positions(i,j)); % Eq. (2.1)Positions(i,j)Best_pos(j)-A*D_Leader; % Eq. (2.2)endelseif p0.5distance2Leaderabs(Best_pos(j)-Positions(i,j));% Eq. (2.5)Positions(i,j)distance2Leader*exp(b.*l).*cos(l.*2*pi)Best_pos(j);endendendtt1;curve(t)Best_Cost;[t Best_Cost]
end
参考资料 [1] https://blog.csdn.net/kjm13182345320/article/details/129215161 [2] https://blog.csdn.net/kjm13182345320/article/details/128105718
