分类预测 | Matlab实现SSA-ELM麻雀优化算法优化极限学习机分类预测
目录
分类效果
基本描述
1.MATLAB实现SSA-ELM麻雀优化算法优化极限学习机分类预测(Matlab完整源码和数据)
2.优化参数为权值和阈值;
3.直接替换数据即可使用,保证程序可正常运行。
4.程序语言为matlab,程序可出分类效果图,迭代优化图,混淆矩阵图。
运行环境matlab2018b及以上。
5.代码特点:参数化编程、参数可方便更改、代码编程思路清晰、注释明细。
程序设计
- 完整程序和数据获取方式(资源出直接下载)Matlab实现SSA-ELM麻雀优化算法优化极限学习机分类预测。
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%% 优化算法
for i = 1: Max_iter
BestF = fitness(1);
R2 = rand(1);
for j = 1 : PDNumber
if(R2 < ST)
X_new(j, :) = pop_new(j, :) .* exp(-j / (rand(1) * Max_iter));
else
X_new(j, :) = pop_new(j, :) + randn() * ones(1, dim);
end
end
for j = PDNumber + 1 : pop
if(j > (pop - PDNumber) / 2 + PDNumber)
X_new(j, :) = randn() .* exp((pop_new(end, :) - pop_new(j, :)) / j^2);
else
A = ones(1, dim);
for a = 1 : dim
if(rand() > 0.5)
A(a) = -1;
end
end
AA = A' / (A * A');
X_new(j, :) = pop_new(1, :) + abs(pop_new(j, :) - pop_new(1, :)) .* AA';
end
end
Temp = randperm(pop);
SDchooseIndex = Temp(1 : SDNumber);
for j = 1 : SDNumber
if(fitness(SDchooseIndex(j)) > BestF)
X_new(SDchooseIndex(j), :) = pop_new(1, :) + randn() .* abs(pop_new(SDchooseIndex(j), :) - pop_new(1, :));
elseif(fitness(SDchooseIndex(j)) == BestF)
K = 2 * rand() -1;
X_new(SDchooseIndex(j), :) = pop_new(SDchooseIndex(j), :) + K .* (abs(pop_new(SDchooseIndex(j), :) - ...
pop_new(end, :)) ./ (fitness(SDchooseIndex(j)) - fitness(end) + 10^-8));
end
end
%% 边界控制
for j = 1 : pop
for a = 1 : dim
if(X_new(j, a) > ub(a))
X_new(j, a) = ub(a);
end
if(X_new(j, a) < lb(a))
X_new(j, a) = lb(a);
end
end
end
%% 获取适应度值
for j = 1 : pop
fitness_new(j) = fobj(X_new(j, :));
end
%% 获取最优种群
for j = 1 : pop
if(fitness_new(j) < GBestF)
GBestF = fitness_new(j);
GBestX = X_new(j, :);
end
end
%% 更新种群和适应度值
pop_new = X_new;
fitness = fitness_new;
%% 更新种群
[fitness, index] = sort(fitness);
for j = 1 : pop
pop_new(j, :) = pop_new(index(j), :);
end
%% 得到优化曲线
curve(i) = GBestF;
avcurve(i) = sum(curve) / length(curve);
end
参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129036772?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128690229