【CNN预测】基于麻雀搜索算法优化CNN实现数据回归预测含Matlab源码
【CNN预测】基于麻雀搜索算法优化CNN实现数据回归预测含Matlab源码
TT_Matlab
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，完整matlab代码或者程序定制加qq1575304183。
1 简介
SSA是一种模仿麻雀捕猎与反捕猎动作的新型智能优化算法，种群成员包括发现者、跟随者和侦察者。种群中适应度较好的发现者将会在搜寻过程中最先收集到食物；有一部分跟随者始终监视发现者，若有跟随者发现种群中有麻雀已经找到更好的食物，这部分跟随者会立即去争夺食物；若负责侦察的麻雀一直处在当前的最优预测位置上，那么只需逃离到当前区域周围的某个地方；若侦察的麻雀并不处在当前最优预测位置上，则寻找当前最优预测区域周围的地方停靠。
2 部分代码
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;
end
Convergence
_curve = zeros(1,Max_
iter);
%Initialize the positions of salps
SalpPositions=initialization(N,dim,ub,lb);
FoodPosition=zeros(1,dim);
FoodFitness=inf;
%calculate the fitness of initial salps
for 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),:);
end
FoodPosition=Sorted_salps(1,:);
FoodFitness=sorted
_salps_
fitness(1);
%Main loop
l=2; % start from the second iteration since the first iteration was dedicated to calculating the fitness of salps
while 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]陈彩虹,王诚. 基于CNN深度算法改进及故障预测研究[J]. 计算机技术与发展, 2020, 30(10):6.
博主简介：擅长智能优化算法、神经网络预测、信号处理、元胞自动机、图像处理、路径规划、无人机等多种领域的Matlab仿真，相关matlab代码问题可私信交流。
部分理论引用网络文献，若有侵权联系博主删除。