分类预测 | MATLAB实现PSO-SVM粒子群算法优化支持向量机多特征分类预测

分类预测 | MATLAB实现PSO-SVM粒子群算法优化支持向量机多特征分类预测

目录

• 分类预测 | MATLAB实现PSO-SVM粒子群算法优化支持向量机多特征分类预测

• 分类效果
• 基本介绍
• 程序设计
• 参考资料

分类效果

基本介绍

MATLAB实现PSO-SVM粒子群算法优化支持向量机多特征分类预测，PSO选择最佳的SVM参数c和g。SVM模型有两个非常重要的参数C与gamma。其中 C是惩罚系数，即对误差的宽容度。c越高，说明越不能容忍出现误差,容易过拟合。C越小，容易欠拟合。C过大或过小，泛化能力变差 。gamma是选择RBF函数作为kernel后，该函数自带的一个参数。隐含地决定了数据映射到新的特征空间后的分布，gamma越大，支持向量越少，gamma值越小，支持向量越多。支持向量的个数影响训练与预测的速度。

程序设计

%%  设置最大速度
Vcmax = pso_option.k * pso_option.popcmax;
Vcmin = -Vcmax ;
Vgmax = pso_option.k * pso_option.popgmax;
Vgmin = -Vgmax ;

%%  误差阈值
eps = 10^(-10);

%%  种群初始化
for i = 1 : pso_option.sizepop
    
    % 随机产生种群和速度
    pop(i, 1) = (pso_option.popcmax - pso_option.popcmin) * rand + pso_option.popcmin;
    pop(i, 2) = (pso_option.popgmax - pso_option.popgmin) * rand + pso_option.popgmin;
    V(i, 1) = Vcmax * rands(1, 1);
    V(i, 2) = Vgmax * rands(1, 1);
    
    % 计算初始适应度
    cmd = [' -v ', num2str(pso_option.v), ' -c ',num2str(pop(i, 1)), ' -g ', num2str(pop(i, 2))];
    fitness(i) = (100 - svmtrain(t_train, p_train, cmd)) / 100;
end

%%  初始化极值和极值点
[global_fitness, bestindex] = min(fitness);   % 全局极值
local_fitness = fitness;                      % 个体极值初始化
global_x = pop(bestindex, :);                 % 全局极值点
local_x = pop;                                % 个体极值点初始化

%%  平均适应度
avgfitness_gen = zeros(1, pso_option.maxgen);

%%  迭代寻优
for i = 1 : pso_option.maxgen
    for j = 1 : pso_option.sizepop
        
       % 速度更新
        V(j, :) = pso_option.wV * V(j, :) + pso_option.c1 * rand * (local_x(j, :) ...
            - pop(j, :)) + pso_option.c2 * rand * (global_x - pop(j, :));
        
        if V(j, 1) > Vcmax
           V(j, 1) = Vcmax;
        end

        if V(j, 1) < Vcmin
           V(j, 1) = Vcmin;
        end

        if V(j, 2) > Vgmax
           V(j, 2) = Vgmax;
        end

        if V(j, 2) < Vgmin
           V(j, 2) = Vgmin;
        end
        
       % 种群更新
        pop(j, :) = pop(j, :) + pso_option.wP * V(j, :);

        if pop(j, 1) > pso_option.popcmax
           pop(j, 1) = pso_option.popcmax;
        end

        if pop(j, 1) < pso_option.popcmin
           pop(j, 1) = pso_option.popcmin;
        end

        if pop(j, 2) > pso_option.popgmax
           pop(j, 2) = pso_option.popgmax;
        end

        if pop(j, 2) < pso_option.popgmin
           pop(j, 2) = pso_option.popgmin;
        end
        
       % 自适应粒子变异
        if rand > 0.5
            k = ceil(2 * rand);

            if k == 1
                pop(j, k) = (20 - 1) * rand + 1;
            end
            
            if k == 2
                pop(j, k) = (pso_option.popgmax - pso_option.popgmin) * rand + pso_option.popgmin;
            end

        end
        
       % 适应度值
       cmd = [' -v ', num2str(pso_option.v), ' -c ', num2str(pop(j, 1)), ' -g ', num2str(pop(j, 2))];
       fitness(j) = (100 - svmtrain(t_train, p_train, cmd)) / 100;
        
       % 个体最优更新
        if fitness(j) < local_fitness(j)
            local_x(j, :) = pop(j, :);
            local_fitness(j) = fitness(j);
        end
        
        if abs(fitness(j)-local_fitness(j)) <= eps && pop(j, 1) < local_x(j, 1)
            local_x(j, :) = pop(j, :);
            local_fitness(j) = fitness(j);
        end
        
       % 群体最优更新
        if fitness(j) < global_fitness
            global_x = pop(j, :);
            global_fitness = fitness(j);
        end
        
        if abs(fitness(j) - global_fitness) <= eps && pop(j, 1) < global_x(1)
            global_x = pop(j, :);
            global_fitness = fitness(j);
        end
        
    end
    
    % 平均适应度和最佳适应度
    fit_gen(i) = global_fitness;
    avgfitness_gen(i) = sum(fitness) / pso_option.sizepop;

end