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
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。文章来源:https://www.toymoban.com/news/detail-691878.html
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。
%% 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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版权声明:本文为CSDN博主「机器学习之心」的原创文章,遵循CC 4.0 BY-SA版权协议,转载请附上原文出处链接及本声明。
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文章来源地址https://www.toymoban.com/news/detail-691878.html
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