回歸預測 | Matlab實現(xiàn)WOA-CNN-SVM鯨魚算法優(yōu)化卷積神經(jīng)網(wǎng)絡-支持向量機的多輸入單輸出回歸預測

效果一覽

基本介紹

1.WOA-CNN-SVM鯨魚算法優(yōu)化卷積神經(jīng)網(wǎng)絡-支持向量機的多變量回歸預測 可直接運行Matlab;
2.評價指標包括: R2、MAE、RMSE和MAPE等，代碼質(zhì)量極高，方便學習和替換數(shù)據(jù)。要求2021版本及以上。
3.鯨魚算法WOA優(yōu)化的參數(shù)為:CNN的批處理大小、學習率、正則化系數(shù)，能夠避免人工選取參數(shù)的盲目性，有效提高其預測精度。
4.main.m為主程序，其他為函數(shù)文件，無需運行，data為數(shù)據(jù)，多輸入單輸出，數(shù)據(jù)回歸預測，輸入7個特征，輸出1個變量，直接替換Excel數(shù)據(jù)即可用!注釋清晰，適合新手小白~

程序設計

• 完整程序和數(shù)據(jù)獲取方式：私信博主回復Matlab實現(xiàn)WOA-CNN-SVM鯨魚算法優(yōu)化卷積神經(jīng)網(wǎng)絡-支持向量機的多輸入單輸出回歸預測；

% 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_pos=zeros(1,dim);
Best_Cost=inf; %change this to -inf for maximization problems%Initialize the positions of search agents
Positions=initialization(pop,dim,ub,lb);curve=zeros(1,Max_iter);t=0;% Loop counter% Main loop
while t<Max_iterfor i=1:size(Positions,1)% Return back the search agents that go beyond the boundaries of the search spaceFlag4ub=Positions(i,:)>ub;Flag4lb=Positions(i,:)<lb;Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;% Calculate objective function for each search agentfitness=fobj(Positions(i,:));% Update the leaderif fitness<Best_Cost % Change this to > for maximization problemBest_Cost=fitness; % Update alphaBest_pos=Positions(i,:);endend

參考資料

[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229