【FNN分类】基于粒子群结合引力搜索算法优化前向反馈神经网络实现数据分类附matlab代码

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提出一种结合粒子群优化(PSO)与引力搜索算法(GSA)的方法，用于优化前馈神经网络(FNN)训练过程。此混合算法旨在提高FNN的全局搜索能力，减少陷入局部最小值的可能性，并加快收敛速度。通过对比实验验证了PSOGSA在训练FNN方面的优越性。

1 内容介绍

引力搜索算法（GSA）是一种基于引力定律和质量相互作用的新型启发式优化方法。实践证明，该算法具有较好的全局最优搜索能力，但在最后一次迭代中存在搜索速度较慢的问题。这项工作提出了粒子群优化 (PSO) 和 GSA 的混合体来解决上述问题。在本文中，GSA 和 PSOGSA 被用作前馈神经网络 (FNN) 的新训练方法，以研究这些算法在减少陷入局部最小值和当前进化学习算法收敛速度慢的问题方面的效率。将结果与标准的基于 PSO 的 FNN 学习算法进行比较。还研究了使用 PSO、GSA 和 PSOGSA 训练的 FNN 的准确度。实验结果表明，在收敛速度和避免局部最小值方面，PSOGSA 在训练 FNN 方面优于 PSO 和 GSA。还证明了用 PSOGSA 训练的 FNN 比用 GSA 训练的 FNN 具有更好的准确性。

2 仿真代码

%% -------------------------------------------------------------------------

clc

clear all

close all

%% ////////////////////////////////////////////////////Data set preparation/////////////////////////////////////////////

load iris.txt

x=sortrows(iris,2);

H2=x(1:150,1);

H3=x(1:150,2);

H4=x(1:150,3);

H5=x(1:150,4);

T=x(1:150,5);

H2=H2';

[xf,PS] = mapminmax(H2);

I2(:,1)=xf;

H3=H3';

[xf,PS2] = mapminmax(H3);

I2(:,2)=xf;

H4=H4';

[xf,PS3] = mapminmax(H4);

I2(:,3)=xf;

H5=H5';

[xf,PS4] = mapminmax(H5);

I2(:,4)=xf;

Thelp=T;

T=T';

[yf,PS5]= mapminmax(T);

T=yf;

T=T';

%% /////////////////////////////////////////////FNN initial parameters//////////////////////////////////////

HiddenNodes=15; %Number of hidden codes

Dim=8*HiddenNodes+3; %Dimension of masses in GSA

TrainingNO=150; %Number of training samples

%% ////////////////////////////////////////////////////////GSA/////////////////////////////////////////////

%Configurations and initializations

noP = 30; %Number of masses

Max_iteration = 500; %Maximum number of iteration

w=2; %Inirtia weight

wMax=0.9; %Max inirtia weight

wMin=0.5; %Min inirtia weight

CurrentFitness =zeros(noP,1);

G0=1; %Gravitational constant

CurrentPosition = rand(noP,Dim); %Postition vector

Velocity = .3*randn(noP,Dim) ; %Velocity vector

acceleration=zeros(noP,Dim); %Acceleration vector

mass(noP)=0; %Mass vector

force=zeros(noP,Dim);%Force vector

%Vectores for saving the location and MSE of the best mass

gBestScore=inf;

gBest=zeros(1,Dim);

ConvergenceCurve=zeros(1,Max_iteration); %Convergence vector

%Main loop

Iteration = 0 ;

while ( Iteration < Max_iteration )

Iteration = Iteration + 1;

G=G0*exp(-20*Iteration/Max_iteration); %Equation (3.3)

force=zeros(noP,Dim);

mass(noP)=0;

acceleration=zeros(noP,Dim);

%Calculate MSEs

for i = 1:noP

for ww=1:(7*HiddenNodes)

Weights(ww)=CurrentPosition(i,ww);

end

for bb=7*HiddenNodes+1:Dim

Biases(bb-(7*HiddenNodes))=CurrentPosition(i,bb);

end

fitness=0;

for pp=1:TrainingNO

actualvalue=My_FNN(4,HiddenNodes,3,Weights,Biases,I2(pp,1),I2(pp,2), I2(pp,3),I2(pp,4));

if(T(pp)==-1)

fitness=fitness+(1-actualvalue(1))^2;

fitness=fitness+(0-actualvalue(2))^2;

fitness=fitness+(0-actualvalue(3))^2;

end

if(T(pp)==0)

fitness=fitness+(0-actualvalue(1))^2;

fitness=fitness+(1-actualvalue(2))^2;

fitness=fitness+(0-actualvalue(3))^2;

end

if(T(pp)==1)

fitness=fitness+(0-actualvalue(1))^2;

fitness=fitness+(0-actualvalue(2))^2;

fitness=fitness+(1-actualvalue(3))^2;

end

fitness=fitness/TrainingNO; %Equation (5.4)

CurrentFitness(i) = fitness;

if(gBestScore>fitness)

gBestScore=fitness;

gBest=CurrentPosition(i,:);

end

best=min(CurrentFitness);%Equation (3.10)

worst=max(CurrentFitness);%Equation (3.11)

for i=1:noP

mass(i)=(CurrentFitness(i)-0.99*worst)/(best-worst);%Equation (3.9)

end

for i=1:noP

mass(i)=mass(i)*5/sum(mass);%Equation (3.14)

end

%Calculate froces

for i=1:noP

for j=1:Dim

for k=1:noP

if(CurrentPosition(k,j)~=CurrentPosition(i,j))

%Equation (3.5)

force(i,j)=force(i,j)+ rand()*G*mass(k)*mass(i)*(CurrentPosition(k,j)-CurrentPosition(i,j))/abs(CurrentPosition(k,j)-CurrentPosition(i,j));

end

%Calculate a

for i=1:noP

for j=1:Dim

if(mass(i)~=0)

acceleration(i,j)=force(i,j)/mass(i);%Equation (3.6)

end

%Update inertia weight

w=wMin-Iteration*(wMax-wMin)/Max_iteration;

%Calculate V

for i=1:noP

for j=1:Dim

%Equation (4.1)

Velocity(i,j)=w*Velocity(i,j)+rand()*acceleration(i,j) + rand()*(gBest(j)-CurrentPosition(i,j));

end

%Calculate X

CurrentPosition = CurrentPosition + Velocity ; %Equation (4.2)

ConvergenceCurve(1,Iteration)=gBestScore;

disp(['PSOGSA is training FNN (Iteration = ', num2str(Iteration),' ,MSE = ', num2str(gBestScore),')'])

end

%% ///////////////////////Calculate the classification//////////////////////

Rrate=0;

Weights=gBest(1:7*HiddenNodes);

Biases=gBest(7*HiddenNodes+1:Dim);

for pp=1:TrainingNO

actualvalue=My_FNN(4,HiddenNodes,3,Weights,Biases,I2(pp,1),I2(pp,2), I2(pp,3),I2(pp,4));

if(T(pp)==-1)

if (round(actualvalue(1))==1 && round(actualvalue(2))==0 && round(actualvalue(3))==0)

Rrate=Rrate+1;

end

if(T(pp)==0)

if (round(actualvalue(1))==0 && round(actualvalue(2))==1 && round(actualvalue(3))==0)

Rrate=Rrate+1;

end

if(T(pp)==1)

if (round(actualvalue(1))==0 && round(actualvalue(2))==0 && round(actualvalue(3))==1)

Rrate=Rrate+1;

end

ClassificationRate=(Rrate/TrainingNO)*100;

disp(['Classification rate = ', num2str(ClassificationRate)]);

%% Draw the convergence curve

hold on;

semilogy(ConvergenceCurve);

title(['Classification rate : ', num2str(ClassificationRate), '%']);

xlabel('Iteration');

ylabel('MSE');

box on

grid on

axis tight

hold off;

3 运行结果

4 参考文献

[1] Mirjalili S A , Hashim S , Sardroudi H M . Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm[J]. Applied Mathematics & Computation, 2012, 218(22):11125-11137.