回歸預(yù)測 | MATLAB實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入單輸出回歸預(yù)測

效果一覽

基本介紹

Matlab實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入回歸預(yù)測（完整源碼和數(shù)據(jù))
1.Matlab實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入單輸出回歸預(yù)測（完整源碼和數(shù)據(jù))
2.輸入多個特征，輸出單個變量，多輸入單輸出回歸預(yù)測；
3.多指標評價，評價指標包括：R2、MAE、MSE、RMSE等，代碼質(zhì)量極高；
4.鯨魚算法優(yōu)化參數(shù)為：優(yōu)化參數(shù)為權(quán)值和閾值；
5.excel數(shù)據(jù)，方便替換，運行環(huán)境2018及以上。

程序設(shè)計

• 完整程序和數(shù)據(jù)下載方式1(資源處直接下載)：MATLAB實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入單輸出回歸預(yù)測
• 完整程序和數(shù)據(jù)下載方式2(訂閱《ELM極限學習機》專欄，同時可閱讀《ELM極限學習機》專欄收錄的所有內(nèi)容，數(shù)據(jù)訂閱后私信我獲取)：MATLAB實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入單輸出回歸預(yù)測
• 完整程序和數(shù)據(jù)下載方式3(訂閱《智能學習》專欄，同時獲取《智能學習》專欄收錄程序3份，數(shù)據(jù)訂閱后私信我獲取)：MATLAB實現(xiàn)WOA-ELM鯨魚算法優(yōu)化極限學習機多輸入單輸出回歸預(yù)測

%%  計算初始適應(yīng)度值
fitness = zeros(1, pop);
for i = 1 : pop
   fitness(i) =  fobj(pop_new(i, :));
end

%%  得到全局最優(yōu)適應(yīng)度值
[fitness, index]= sort(fitness);
GBestF = fitness(1); 

%%  得到全局最優(yōu)種群
for i = 1 : pop
    pop_new(i, :) = pop_lsat(index(i), :);
end

GBestX = pop_new(1, :);
X_new  = pop_new;

%%  優(yōu)化算法
for i = 1: Max_iter

   BestF = fitness(1);
   R2 = rand(1);

   for j = 1 : PDNumber
      if(R2 < ST)
          X_new(j, :) = pop_new(j, :) .* exp(-j / (rand(1) * Max_iter));
      else
          X_new(j, :) = pop_new(j, :) + randn() * ones(1, dim);
      end     
   end
   
   for j = PDNumber + 1 : pop
        if(j > (pop - PDNumber) / 2 + PDNumber)
          X_new(j, :) = randn() .* exp((pop_new(end, :) - pop_new(j, :)) / j^2);
        else
          A = ones(1, dim);
          for a = 1 : dim
              if(rand() > 0.5)
                A(a) = -1;
              end
          end
          AA = A' / (A * A');     
          X_new(j, :) = pop_new(1, :) + abs(pop_new(j, :) - pop_new(1, :)) .* AA';
       end
   end
   
   Temp = randperm(pop);
   SDchooseIndex = Temp(1 : SDNumber); 
   
   for j = 1 : SDNumber
       if(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));
       end
   end

%%  邊界控制
   for j = 1 : pop
       for a = 1 : dim
           if(X_new(j, a) > ub(a))
              X_new(j, a) = ub(a);
           end
           if(X_new(j, a) < lb(a))
              X_new(j, a) = lb(a);
           end
       end
   end 

%%  獲取適應(yīng)度值
   for j = 1 : pop
    fitness_new(j) = fobj(X_new(j, :));
   end
   
%%  獲取最優(yōu)種群
   for j = 1 : pop
       if(fitness_new(j) < GBestF)
          GBestF = fitness_new(j);
          GBestX = X_new(j, :);
       end
   end
   
%%  更新種群和適應(yīng)度值
   pop_new = X_new;
   fitness = fitness_new;

%%  更新種群 
   [fitness, index] = sort(fitness);
   for j = 1 : pop
      pop_new(j, :) = pop_new(index(j), :);
   end

%%  得到優(yōu)化曲線
   curve(i) = GBestF;
   avcurve(i) = sum(curve) / length(curve);
end

%%  得到最優(yōu)值
Best_pos = GBestX;
Best_score = curve(end);

參考資料

[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718文章來源地址http://www.zghlxwxcb.cn/news/detail-616649.html

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