多输入多输出 | MATLAB实现PSO-LSSVM粒子群优化最小二乘支持向量机多输入多输出

多输入多输出 | MATLAB实现PSO-LSSVM粒子群优化最小二乘支持向量机多输入多输出

预测效果

基本介绍

MATLAB实现PSO-LSSVM粒子群优化最小二乘支持向量机多输入多输出
1.data为数据集，10个输入特征，3个输出变量。
2.main.m为主程序文件。
3.命令窗口输出MBE、MAE和R2，可在下载区获取数据和程序内容。

程序设计

• 完整程序和数据下载方式：私信博主回复MATLAB实现PSO-LSSVM粒子群优化最小二乘支持向量机多输入多输出。

%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  参数初始化
c1      = 4.494;       % 学习因子
c2      = 4.494;       % 学习因子
maxgen  =   50;        % 种群更新次数  
sizepop =    5;        % 种群规模
Vmax    =  1.0;        % 最大速度
Vmin    = -1.0;        % 最小速度
popmax  =  1.0;        % 最大边界
popmin  = -1.0;        % 最小边界
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
for i = 1 : sizepoppop(i, :) = rands(1, numsum);  % 初始化种群V(i, :) = rands(1, numsum);    % 初始化速度fitness(i) = fun(pop(i, :), hiddennum, net, p_train, t_train);
end
%-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
%%  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 : 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;% 种群更新pop(j, :) = pop(j, :) + 0.2 * V(j, :);pop(j, (pop(j, :) > popmax)) = popmax;pop(j, (pop(j, :) < popmin)) = popmin;% 适应度值fitness(j) = fun(pop(j, :), hiddennum, net, p_train, t_train);endfor j = 1 : sizepop% 个体最优更新if fitness(j) < fitnessgbest(j)gbest(j, :) = pop(j, :);fitnessgbest(j) = fitness(j);endendBestFit = [BestFit, fitnesszbest];    
end

往期精彩

MATLAB实现RBF径向基神经网络多输入多输出预测
MATLAB实现BP神经网络多输入多输出预测
MATLAB实现DNN神经网络多输入多输出预测

参考资料

[1] https://blog.csdn.net/kjm13182345320/article/details/116377961
[2] https://blog.csdn.net/kjm13182345320/article/details/127931217
[3] https://blog.csdn.net/kjm13182345320/article/details/127894261