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【BP分类】基于鲸鱼算法优化BP神经网络实现数据分类matlab代码

来源：互联网收集：自由互联发布时间：2022-06-18

1 简介随着人类社会的进步,科学技术的发展日新月异.模拟人脑神经网络的人工神经网络已取得了长足的发展.经过半个多世纪的发展,人工神经网络在计算机科学,人工智能,智能控制等方

1 简介

随着人类社会的进步,科学技术的发展日新月异.模拟人脑神经网络的人工神经网络已取得了长足的发展.经过半个多世纪的发展,人工神经网络在计算机科学,人工智能,智能控制等方面得到了广泛的应用. 当代社会是一个讲究效率的社会,科技更新领域也是如此.在人工神经网络研究领域,算法的优化显得尤为重要,对提高网络整体性能举足轻重.BP神经网络模型是目前应用最为广泛的一种神经网络模型,对于解决非线性复杂问题具有重要的意义.但是BP神经网络有其自身的一些不足(收敛速度慢和容易陷入局部极小值问题),在解决某些现实问题的时候显得力不从心.针对这个问题,本文利用遗传算法的并行全局搜索的优势,能够弥补BP网络的不足,为解决大规模复杂问题提供了广阔的前景.本文将鲸鱼算法与BP网络有机地结合起来,提出了一种新的网络结构,在稳定性,学习性和效率方面都有了很大的提高. 基于以上的研究目的,本文首先设计了BP神经网络结构,在此基础上,应用鲸鱼算法进行优化,达到了加快收敛速度和全局寻优的效果.本文借助MATLAB平台,对算法的优化内容进行了仿真实验,得出的效果也符合期望值,实现了对BP算法优化的目的.

2 部分代码

%_________________________________________________________________________%
% Whale Optimization Algorithm (WOA) source codes demo 1.0 %
% %
% %
%_________________________________________________________________________%
% You can simply define your cost in a seperate file and load its handle to fobj
% The initial parameters that you need are:
%__________________________________________
% fobj = @YourCostFunction
% dim = number of your variables
% Max_iteration = maximum number of generations
% SearchAgents_no = number of search agents
% lb=[lb1,lb2,...,lbn] where lbn is the lower bound of variable n
% ub=[ub1,ub2,...,ubn] where ubn is the upper bound of variable n
% If all the variables have equal lower bound you can just
% define lb and ub as two single number numbers
% To run ALO: [Best_score,Best_pos,cg_curve]=ALO(SearchAgents_no,Max_iteration,lb,ub,dim,fobj)
% The Whale Optimization Algorithm
function [Leader_score,Leader_pos,Convergence_curve]=WOA(SearchAgents_no,Max_iter,lb,ub,dim,fobj,handles,value)
% initialize position vector and score for the leader
Leader_pos=zeros(1,dim);
Leader_score=inf; %change this to -inf for maximization problems
%Initialize the positions of search agents
Positions=initialization(SearchAgents_no,dim,ub,lb);
Convergence_curve=zeros(1,Max_iter);
t=0;% Loop counter
% Main loop
while t<Max_iter
for i=1:size(Positions,1)
% Return back the search agents that go beyond the boundaries of the search space
Flag4ub=Positions(i,:)>ub;
Flag4lb=Positions(i,:)<lb;
Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
% Calculate objective function for each search agent
fitness=fobj(Positions(i,:));
All_fitness(1,i)=fitness;
% Update the leader
if fitness<Leader_score % Change this to > for maximization problem
Leader_score=fitness; % Update alpha
Leader_pos=Positions(i,:);
end
end
a=2-t*((2)/Max_iter); % a decreases linearly fron 2 to 0 in Eq. (2.3)
% a2 linearly dicreases from -1 to -2 to calculate t in Eq. (3.12)
a2=-1+t*((-1)/Max_iter);
% Update the Position of search agents
for i=1:size(Positions,1)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
A=2*a*r1-a; % Eq. (2.3) in the paper
C=2*r2; % Eq. (2.4) in the paper
b=1; % parameters in Eq. (2.5)
l=(a2-1)*rand+1; % parameters in Eq. (2.5)
p = rand(); % p in Eq. (2.6)
for j=1:size(Positions,2)
if p<0.5
if abs(A)>=1
rand_leader_index = floor(SearchAgents_no*rand()+1);
X_rand = Positions(rand_leader_index, :);
D_X_rand=abs(C*X_rand(j)-Positions(i,j)); % Eq. (2.7)
Positions(i,j)=X_rand(j)-A*D_X_rand; % Eq. (2.8)
elseif abs(A)<1
D_Leader=abs(C*Leader_pos(j)-Positions(i,j)); % Eq. (2.1)
Positions(i,j)=Leader_pos(j)-A*D_Leader; % Eq. (2.2)
end
elseif p>=0.5
distance2Leader=abs(Leader_pos(j)-Positions(i,j));
% Eq. (2.5)
Positions(i,j)=distance2Leader*exp(b.*l).*cos(l.*2*pi)+Leader_pos(j);
end
end
end
t=t+1;
Convergence_curve(t)=Leader_score;
if t>2
line([t-1 t], [Convergence_curve(t-1) Convergence_curve(t)],'Color','b')
xlabel('Iteration');
ylabel('Best score obtained so far');
drawnow
end
set(handles.itertext,'String', ['The current iteration is ', num2str(t)])
set(handles.optimumtext,'String', ['The current optimal value is ', num2str(Leader_score)])
if value==1
hold on
scatter(t*ones(1,SearchAgents_no),All_fitness,'.','k')
end
end