粒子群优化SVM的惩罚系数和和函数参数,以均方误差为适应度函数,编写matlab代码,以及适应度函数
时间: 2023-07-04 14:07:50 浏览: 68
以下是粒子群优化SVM的惩罚系数和函数参数的matlab代码:
```matlab
% 粒子群优化SVM的惩罚系数和函数参数
% 以均方误差为适应度函数
clear all;
close all;
% 数据集
load fisheriris
X = meas(:,1:2);
Y = species;
% SVM模型
svmmodel = fitcsvm(X,Y,'KernelFunction','rbf');
% 粒子群算法参数
nVar = 3; % 变量数量:惩罚系数和两个函数参数
VarSize = [1 nVar]; % 变量大小
VarMin = [0 0.1 0.1]; % 变量最小值
VarMax = [10 10 10]; % 变量最大值
MaxIt = 100; % 最大迭代次数
nPop = 50; % 粒子数
w = 1; % 惯性权重
wdamp = 0.99; % 惯性权重衰减系数
c1 = 2; % 个体学习因子
c2 = 2; % 全局学习因子
% 初始化粒子
empty_particle.Position = [];
empty_particle.Velocity = [];
empty_particle.Cost = [];
empty_particle.Best.Position = [];
empty_particle.Best.Cost = [];
particle = repmat(empty_particle, nPop, 1);
GlobalBest.Cost = inf;
for i = 1:nPop
% 随机初始化粒子位置
particle(i).Position = unifrnd(VarMin, VarMax, VarSize);
% 初始化粒子速度
particle(i).Velocity = zeros(VarSize);
% 计算粒子适应度
[particle(i).Cost, ~] = SVMCost(particle(i).Position, svmmodel, X, Y);
% 更新个体最优位置
particle(i).Best.Position = particle(i).Position;
particle(i).Best.Cost = particle(i).Cost;
% 更新全局最优位置
if particle(i).Best.Cost < GlobalBest.Cost
GlobalBest = particle(i).Best;
end
end
BestCosts = zeros(MaxIt, 1);
% 粒子群优化主循环
for it = 1:MaxIt
for i = 1:nPop
% 更新粒子速度
particle(i).Velocity = w*particle(i).Velocity ...
+ c1*rand(VarSize).*(particle(i).Best.Position - particle(i).Position) ...
+ c2*rand(VarSize).*(GlobalBest.Position - particle(i).Position);
% 限制速度范围
particle(i).Velocity = max(particle(i).Velocity, VarMin);
particle(i).Velocity = min(particle(i).Velocity, VarMax);
% 更新粒子位置
particle(i).Position = particle(i).Position + particle(i).Velocity;
% 限制位置范围
particle(i).Position = max(particle(i).Position, VarMin);
particle(i).Position = min(particle(i).Position, VarMax);
% 计算粒子适应度
[particle(i).Cost, ~] = SVMCost(particle(i).Position, svmmodel, X, Y);
% 更新个体最优位置
if particle(i).Cost < particle(i).Best.Cost
particle(i).Best.Position = particle(i).Position;
particle(i).Best.Cost = particle(i).Cost;
% 更新全局最优位置
if particle(i).Best.Cost < GlobalBest.Cost
GlobalBest = particle(i).Best;
end
end
end
% 记录最优适应度值
BestCosts(it) = GlobalBest.Cost;
% 显示迭代信息
disp(['迭代次数:' num2str(it) ',最优适应度值:' num2str(BestCosts(it))]);
% 惯性权重衰减
w = w * wdamp;
end
% 绘制适应度曲线
figure;
plot(BestCosts, 'LineWidth', 2);
xlabel('迭代次数');
ylabel('最优适应度值');
grid on;
% 输出最优解
disp(['最优解:' num2str(GlobalBest.Position)]);
disp(['最优适应度值:' num2str(GlobalBest.Cost)]);
% SVM适应度函数
function [cost, model] = SVMCost(params, svmmodel, X, Y)
% 训练SVM模型
C = params(1);
sigma = params(2);
gamma = params(3);
model = fitcsvm(X, Y, 'KernelFunction', 'rbf', 'BoxConstraint', C, 'KernelScale', sigma, 'KernelFunction', @(X,Y) exp(-gamma*norm(X-Y)^2));
% 计算预测误差
Ypred = predict(model, X);
cost = mse(Y-Ypred);
end
```
其中,SVM适应度函数用于计算SVM模型的预测误差,并将其作为适应度值返回。以下是SVM适应度函数的代码:
```matlab
function [cost, model] = SVMCost(params, svmmodel, X, Y)
% 训练SVM模型
C = params(1);
sigma = params(2);
gamma = params(3);
model = fitcsvm(X, Y, 'KernelFunction', 'rbf', 'BoxConstraint', C, 'KernelScale', sigma, 'KernelFunction', @(X,Y) exp(-gamma*norm(X-Y)^2));
% 计算预测误差
Ypred = predict(model, X);
cost = mse(Y-Ypred);
end
```
其中,params为粒子的变量值,svmmodel为已经训练好的SVM模型,X和Y为训练数据集。C、sigma和gamma分别为惩罚系数和两个函数参数。