回归预测 | Matlab基于SO-LSTM蛇群算法优化长短期记忆神经网络的数据多输入单输出回归预测
目录
效果一览
基本介绍
1.Matlab基于SO-LSTM蛇群算法优化长短期记忆神经网络的数据多输入单输出回归预测（完整源码和数据）；
2.优化参数为：学习率，隐含层节点，正则化参数。
3.多特征输入单输出的回归预测。程序内注释详细，直接替换数据就可以用。
4.程序语言为matlab，程序可出预测效果图，迭代优化图，相关分析图，运行环境matlab2020b及以上。评价指标包括:R2、MAE、MSE、RMSE和MAPE等。
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
- 完整源码和数据获取方式(资源处下载)：Matlab基于SO-LSTM蛇群算法优化长短期记忆神经网络的数据多输入单输出回归预测。
clike
function [fval,Xfood,gbest_t] = SO(N,T,lb,ub,dim,fobj)
%initial
vec_flag=[1,-1];
Threshold=0.25;
Thresold2= 0.6;
C1=0.5;
C2=.05;
C3=2;
X=initialization(N,dim,ub,lb);
for i=1:N
fitness(i)=feval(fobj,X(i,:));
end
[GYbest, gbest] = min(fitness);
Xfood = X(gbest,:);
%Diving the swarm into two equal groups males and females
Nm=round(N/2);%eq.(2&3)
Nf=N-Nm;
Xm=X(1:Nm,:);
Xf=X(Nm+1:N,:);
fitness_m=fitness(1:Nm);
fitness_f=fitness(Nm+1:N);
[fitnessBest_m, gbest1] = min(fitness_m);
Xbest_m = Xm(gbest1,:);
[fitnessBest_f, gbest2] = min(fitness_f);
Xbest_f = Xf(gbest2,:);
for t = 1:T
disp([' ',num2str(t),' ε '])
Temp=exp(-((t)/T)); %eq.(4)
Q=C1*exp(((t-T)/(T)));%eq.(5)
if Q>1 Q=1; end
% Exploration Phase (no Food)
if Q<Threshold
for i=1:Nm
for j=1:1:dim
rand_leader_index = floor(Nm*rand()+1);
X_randm = Xm(rand_leader_index, :);
flag_index = floor(2*rand()+1);
Flag=vec_flag(flag_index);
Am=exp(-fitness_m(rand_leader_index)/(fitness_m(i)+eps));%eq.(7)
Xnewm(i,j)=X_randm(j)+Flag*C2*Am*((ub(j)-lb(j))*rand+lb(j));%eq.(6)
end
end
for i=1:Nf
for j=1:1:dim
rand_leader_index = floor(Nf*rand()+1);
X_randf = Xf(rand_leader_index, :);
flag_index = floor(2*rand()+1);
Flag=vec_flag(flag_index);
Af=exp(-fitness_f(rand_leader_index)/(fitness_f(i)+eps));%eq.(9)
Xnewf(i,j)=X_randf(j)+Flag*C2*Af*((ub(j)-lb(j))*rand+lb(j));%eq.(8)
end
end
else %Exploitation Phase (Food Exists)
if Temp>Thresold2 %hot
for i=1:Nm
flag_index = floor(2*rand()+1);
Flag=vec_flag(flag_index);
for j=1:1:dim
Xnewm(i,j)=Xfood(j)+C3*Flag*Temp*rand*(Xfood(j)-Xm(i,j));%eq.(10)
end
end
for i=1:Nf
flag_index = floor(2*rand()+1);
Flag=vec_flag(flag_index);
for j=1:1:dim
Xnewf(i,j)=Xfood(j)+Flag*C3*Temp*rand*(Xfood(j)-Xf(i,j));%eq.(10)
end
end
else %cold
if rand>0.6 %fight
for i=1:Nm
for j=1:1:dim
FM=exp(-(fitnessBest_f)/(fitness_m(i)+eps));%eq.(13)
Xnewm(i,j)=Xm(i,j) +C3*FM*rand*(Q*Xbest_f(j)-Xm(i,j));%eq.(11)
end
end
for i=1:Nf
for j=1:1:dim
FF=exp(-(fitnessBest_m)/(fitness_f(i)+eps));%eq.(14)
Xnewf(i,j)=Xf(i,j)+C3*FF*rand*(Q*Xbest_m(j)-Xf(i,j));%eq.(12)
end
end
else%mating
for i=1:Nm
for j=1:1:dim
Mm=exp(-fitness_f(i)/(fitness_m(i)+eps));%eq.(17)
Xnewm(i,j)=Xm(i,j) +C3*rand*Mm*(Q*Xf(i,j)-Xm(i,j));%eq.(15
end
end
for i=1:Nf
for j=1:1:dim
Mf=exp(-fitness_m(i)/(fitness_f(i)+eps));%eq.(18)
Xnewf(i,j)=Xf(i,j) +C3*rand*Mf*(Q*Xm(i,j)-Xf(i,j));%eq.(16)
end
end参考资料
[1] https://blog.csdn.net/kjm13182345320/article/details/129215161
[2] https://blog.csdn.net/kjm13182345320/article/details/128105718