【BP分类】基于麻雀算法优化BP神经网络实现数据分类matlab代码

1 简介

提出了一种基于麻雀搜索算法优化的BP神经网络数据分类方法.该方法兼顾了麻雀算法和梯度下降优化算法分别在全局和局部搜索极小点的优势;避免了在BP网络训练过程中过早收敛于局部极小点的风险;与BP算法相比,该算法多次重复过程所得网络的均方差比较稳定.在算法验证中,该算法不但有较高的执行效率,也能达到很高的分类精度.

2 部分代码

%_________________________________________________________________________________%  Salp Swarm Algorithm (SSA) source codes version 1.0​%​​_______________________​function [FoodFitness,FoodPosition,Convergence_curve]=SSA(N,Max_iter,lb,ub,dim,fobj)if size(ub,1)==1    ub=ones(dim,1)*ub;    lb=ones(dim,1)*lb;endConvergence_curve = zeros(1,Max_iter);%Initialize the positions of salpsSalpPositions=initialization(N,dim,ub,lb);FoodPosition=zeros(1,dim);FoodFitness=inf;%calculate the fitness of initial salpsfor i=1:size(SalpPositions,1)    SalpFitness(1,i)=fobj(SalpPositions(i,:));end[sorted_salps_fitness,sorted_indexes]=sort(SalpFitness);for newindex=1:N    Sorted_salps(newindex,:)=SalpPositions(sorted_indexes(newindex),:);endFoodPosition=Sorted_salps(1,:);FoodFitness=sorted_salps_fitness(1);%Main loopl=2; % start from the second iteration since the first iteration was dedicated to calculating the fitness of salpswhile l<Max_iter+1    c1 = 2*exp(-(4*l/Max_iter)^2); % Eq. (3.2) in the paper    for i=1:size(SalpPositions,1)        SalpPositions= SalpPositions';        if i<=N/2            for j=1:1:dim                c2=rand();                c3=rand();                %%%%%%%%%%%%% % Eq. (3.1) in the paper %%%%%%%%%%%%%%                if c3<0.5                     SalpPositions(j,i)=FoodPosition(j)+c1*((ub(j)-lb(j))*c2+lb(j));                else                    SalpPositions(j,i)=FoodPosition(j)-c1*((ub(j)-lb(j))*c2+lb(j));                end                %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%            end        elseif i>N/2 && i<N+1            point1=SalpPositions(:,i-1);            point2=SalpPositions(:,i);            SalpPositions(:,i)=(point2+point1)/2; % % Eq. (3.4) in the paper        end        SalpPositions= SalpPositions';    end    for i=1:size(SalpPositions,1)        Tp=SalpPositions(i,:)>ub';Tm=SalpPositions(i,:)<lb';SalpPositions(i,:)=(SalpPositions(i,:).*(~(Tp+Tm)))+ub'.*Tp+lb'.*Tm;        SalpFitness(1,i)=fobj(SalpPositions(i,:));        if SalpFitness(1,i)<FoodFitness            FoodPosition=SalpPositions(i,:);            FoodFitness=SalpFitness(1,i);         end    end    Convergence_curve(l)=FoodFitness;    l = l + 1;end​

3 仿真结果

4 参考文献

[1]孟彩霞, 吴迪, 雷雨. 基于麻雀搜索算法优化的BP神经网络卫星钟差预报[J]. 大地测量与地球动力学, 2022, 42(2):7.

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部分理论引用网络文献，若有侵权联系博主删除。