[人工智能]【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

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;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]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。

?

创作打卡挑战赛

赢取流量/现金/CSDN周边激励大奖