回归预测 | Matlab基于SO-BiLSTM蛇群算法优化双向长短期记忆神经网络的数据多输入单输出回归预测

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

效果一览

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基本介绍

1.Matlab基于SO-BiLSTM蛇群算法优化双向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。

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程序设计

完整源码和数据获取方式(资源处下载)：Matlab基于SO-BiLSTM蛇群算法优化双向长短期记忆神经网络的数据多输入单输出回归预测。

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