分類預(yù)測 | MATLAB實現(xiàn)SSA-CNN-SVM基于麻雀算法優(yōu)化卷積支持向量機分類預(yù)測

預(yù)測效果

基本介紹

MATLAB實現(xiàn)SSA-CNN-SVM基于麻雀算法優(yōu)化卷積支持向量機分類預(yù)測，優(yōu)化參數(shù)為：學習率，批量處理大小，正則化參數(shù)。圖很多，包括分類效果圖，迭代優(yōu)化圖，混淆矩陣圖。

程序設(shè)計

• 完整程序和數(shù)據(jù)獲取方式1：同等價值程序兌換；
• 完整程序和數(shù)據(jù)獲取方式2：私信博主回復 MATLAB實現(xiàn)SSA-CNN-SVM基于麻雀算法優(yōu)化卷積支持向量機分類預(yù)測獲取。

%%  劃分訓練集和測試集
P_train = res(1: num_train_s, 1: f_)';
T_train = res(1: num_train_s, f_ + 1: end)';
M = size(P_train, 2);P_test = res(num_train_s + 1: end, 1: f_)';
T_test = res(num_train_s + 1: end, f_ + 1: end)';
N = size(P_test, 2);%%  數(shù)據(jù)歸一化
[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);
%%  個體極值和群體極值
[fitnesszbest, bestindex] = min(fitness);
zbest = pop(bestindex, :);     % 全局最佳
gbest = pop;                   % 個體最佳
fitnessgbest = fitness;        % 個體最佳適應(yīng)度值
BestFit = fitnesszbest;        % 全局最佳適應(yīng)度值%%  迭代尋優(yōu)
for i = 1 : maxgenfor j = 1 : sizepop% 速度更新V(j, :) = V(j, :) + c1 * rand * (gbest(j, :) - pop(j, :)) + c2 * rand * (zbest - pop(j, :));V(j, (V(j, :) > Vmax)) = Vmax;V(j, (V(j, :) < Vmin)) = Vmin;% 種群更新pop(j, :) = pop(j, :) + 0.2 * V(j, :);pop(j, (pop(j, :) > popmax)) = popmax;pop(j, (pop(j, :) < popmin)) = popmin;% 自適應(yīng)變異pos = unidrnd(numsum);if rand > 0.95pop(j, pos) = rands(1, 1);end% 適應(yīng)度值fitness(j) = fun(pop(j, :), hiddennum, net, p_train, t_train);endfor j = 1 : sizepop% 個體最優(yōu)更新if fitness(j) < fitnessgbest(j)gbest(j, :) = pop(j, :);fitnessgbest(j) = fitness(j);end% 群體最優(yōu)更新 if fitness(j) < fitnesszbestzbest = pop(j, :);fitnesszbest = fitness(j);endendBestFit = [BestFit, fitnesszbest];    
end
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版權(quán)聲明：本文為CSDN博主「機器學習之心」的原創(chuàng)文章，遵循CC 4.0 BY-SA版權(quán)協(xié)議，轉(zhuǎn)載請附上原文出處鏈接及本聲明。
原文鏈接：https://blog.csdn.net/kjm13182345320/article/details/130462492

參考資料

[1] https://blog.csdn.net/kjm13182345320/article/details/129679476?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/129659229?spm=1001.2014.3001.5501
[3] https://blog.csdn.net/kjm13182345320/article/details/129653829?spm=1001.2014.3001.5501