【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
3 仿真结果
%_______________________________________________________________________________
__% 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);FoodFit
ness=inf;%calculate the fitness of initial salpsfor i=1:size(SalpPositions,1)
SalpFitness(1,i)=fobj(SalpPositions(i,:));end[sorted_salps_fitness,sorted_indexe
s]=sort(SalpFitness);for newindex=1:N
Sorted_salps(newindex,:)=SalpPositions(sorted_indexes(newindex),:);endFoodPositi
on=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
