分类预测 | MATLAB实现SSA-CNN-SVM基于麻雀算法优化卷积支持向量机分类预测
目录
预测效果
基本介绍
MATLAB实现SSA-CNN-SVM基于麻雀算法优化卷积支持向量机分类预测，优化参数为：学习率，批量处理大小，正则化参数。图很多，包括分类效果图，迭代优化图，混淆矩阵图。
程序设计
- 完整程序和数据获取方式1：同等价值程序兑换；
- 完整程序和数据获取方式2：私信博主回复 MATLAB实现SSA-CNN-SVM基于麻雀算法优化卷积支持向量机分类预测获取。
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%% 划分训练集和测试集
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);
%% 数据归一化
[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);
%% 个体极值和群体极值
[fitnesszbest, bestindex] = min(fitness);
zbest = pop(bestindex, :); % 全局最佳
gbest = pop; % 个体最佳
fitnessgbest = fitness; % 个体最佳适应度值
BestFit = fitnesszbest; % 全局最佳适应度值
%% 迭代寻优
for i = 1 : maxgen
for 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;
% 种群更新
pop(j, :) = pop(j, :) + 0.2 * V(j, :);
pop(j, (pop(j, :) > popmax)) = popmax;
pop(j, (pop(j, :) < popmin)) = popmin;
% 自适应变异
pos = unidrnd(numsum);
if rand > 0.95
pop(j, pos) = rands(1, 1);
end
% 适应度值
fitness(j) = fun(pop(j, :), hiddennum, net, p_train, t_train);
end
for j = 1 : sizepop
% 个体最优更新
if fitness(j) < fitnessgbest(j)
gbest(j, :) = pop(j, :);
fitnessgbest(j) = fitness(j);
end
% 群体最优更新
if fitness(j) < fitnesszbest
zbest = pop(j, :);
fitnesszbest = fitness(j);
end
end
BestFit = [BestFit, fitnesszbest];
end
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原文链接：https://blog.csdn.net/kjm13182345320/article/details/130462492参考资料
[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