回归预测 | MATLAB实现SSA-ELM麻雀算法优化极限学习机多输入单输出回归预测
目录
- 回归预测 | MATLAB实现SSA-ELM麻雀算法优化极限学习机多输入单输出回归预测
- 效果一览
- 基本介绍
- 程序设计
- 参考资料
效果一览
基本介绍
1.MATLAB实现SSA-ELM麻雀算法优化极限学习机多输入单输出回归预测;
2.输入7个特征,输出1个,即多输入单输出;
3.运行环境Matlab2018及以上,运行主程序main即可,其余为函数文件无需运行,所有程序放在一个文件夹,data为数据集;
4.SSA-ELM麻雀算法优化极限学习机权值和偏置,命令窗口输出RMSE、MAE、R2、MAPE等评价指标。
程序设计
function [Best_pos, Best_score, curve, avcurve] = SSA(pop, Max_iter, lb, ub, dim, fobj)
%% 参数设置
ST = 0.8; % 预警值
PD = 0.2; % 发现者的比列,剩下的是加入者
PDNumber = pop * PD; % 发现者数量
SDNumber = pop - pop * PD; % 意识到有危险麻雀数量
%% 判断优化参数个数
if(max(size(ub)) == 1)
ub = ub .* ones(1, dim);
lb = lb .* ones(1, dim);
end
%% 种群初始化
pop_lsat = initialization(pop, dim, ub, lb);
pop_new = pop_lsat;
%% 计算初始适应度值
fitness = zeros(1, pop);
for i = 1 : pop
fitness(i) = fobj(pop_new(i, :));
end
%% 得到全局最优适应度值
[fitness, index]= sort(fitness);
GBestF = fitness(1);
%% 得到全局最优种群
for i = 1 : pop
pop_new(i, :) = pop_lsat(index(i), :);
end
GBestX = pop_new(1, :);
X_new = pop_new;
%% 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
%% 得到最优值
Best_pos = GBestX;
Best_score = curve(end);