回归预测 | MATLAB实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测

回归预测 | MATLAB实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测

效果一览

基本介绍

Matlab实现WOA-ELM鲸鱼算法优化极限学习机多输入回归预测（完整源码和数据)
1.Matlab实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测（完整源码和数据)
2.输入多个特征，输出单个变量，多输入单输出回归预测；
3.多指标评价，评价指标包括：R2、MAE、MSE、RMSE等，代码质量极高；
4.鲸鱼算法优化参数为：优化参数为权值和阈值；
5.excel数据，方便替换，运行环境2018及以上。

程序设计

• 完整程序和数据下载方式1(资源处直接下载)：MATLAB实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测
• 完整程序和数据下载方式2(订阅《ELM极限学习机》专栏，同时可阅读《ELM极限学习机》专栏收录的所有内容，数据订阅后私信我获取)：MATLAB实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测
• 完整程序和数据下载方式3(订阅《智能学习》专栏，同时获取《智能学习》专栏收录程序3份，数据订阅后私信我获取)：MATLAB实现WOA-ELM鲸鱼算法优化极限学习机多输入单输出回归预测

%%  计算初始适应度值
fitness = zeros(1, pop);
for i = 1 : popfitness(i) =  fobj(pop_new(i, :));
end%%  得到全局最优适应度值
[fitness, index]= sort(fitness);
GBestF = fitness(1); %%  得到全局最优种群
for i = 1 : poppop_new(i, :) = pop_lsat(index(i), :);
endGBestX = pop_new(1, :);
X_new  = pop_new;%%  优化算法
for i = 1: Max_iterBestF = fitness(1);R2 = rand(1);for j = 1 : PDNumberif(R2 < ST)X_new(j, :) = pop_new(j, :) .* exp(-j / (rand(1) * Max_iter));elseX_new(j, :) = pop_new(j, :) + randn() * ones(1, dim);end     endfor j = PDNumber + 1 : popif(j > (pop - PDNumber) / 2 + PDNumber)X_new(j, :) = randn() .* exp((pop_new(end, :) - pop_new(j, :)) / j^2);elseA = ones(1, dim);for a = 1 : dimif(rand() > 0.5)A(a) = -1;endendAA = A' / (A * A');     X_new(j, :) = pop_new(1, :) + abs(pop_new(j, :) - pop_new(1, :)) .* AA';endendTemp = randperm(pop);SDchooseIndex = Temp(1 : SDNumber); for j = 1 : SDNumberif(fitness(SDchooseIndex(j)) > BestF)X_new(SDchooseIndex(j), :) = pop_new(1, :) + randn() .* abs(pop_new(SDchooseIndex(j), :) - pop_new(1, :));elseif(fitness(SDchooseIndex(j)) == BestF)K = 2 * rand() -1;X_new(SDchooseIndex(j), :) = pop_new(SDchooseIndex(j), :) + K .* (abs(pop_new(SDchooseIndex(j), :) - ...pop_new(end, :)) ./ (fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));endend%%  边界控制for j = 1 : popfor a = 1 : dimif(X_new(j, a) > ub(a))X_new(j, a) = ub(a);endif(X_new(j, a) < lb(a))X_new(j, a) = lb(a);endendend %%  获取适应度值for j = 1 : popfitness_new(j) = fobj(X_new(j, :));end%%  获取最优种群for j = 1 : popif(fitness_new(j) < GBestF)GBestF = fitness_new(j);GBestX = X_new(j, :);endend%%  更新种群和适应度值pop_new = X_new;fitness = fitness_new;%%  更新种群 [fitness, index] = sort(fitness);for j = 1 : poppop_new(j, :) = pop_new(index(j), :);end%%  得到优化曲线curve(i) = GBestF;avcurve(i) = sum(curve) / length(curve);
end%%  得到最优值
Best_pos = GBestX;
Best_score = curve(end);

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718