💥1 概述
本文利用粒子群优化（PSO）和引力搜索算法（GSA）的混合体，称为PSOGSA，用于训练前馈神经网络（FNN）。该算法应用于众所周知的鸢尾花数据集。该程序是一个改进的前馈神经网络，使用称为PSOGSA的混合算法。
文献来源：
肤色识别的应用在基于内容的分析和人机交互中的应用得到了扩展。因此，实现一种有用的方法来分割皮肤像素可以帮助解决所呈现的问题。该文提出一种混合PSOGSA -ANN作为前馈神经网络（FNNs）的新训练方法，以研究解决皮肤分类问题的高效率。所提出的颜色分割算法直接应用于RGB色彩空间，无需色彩空间转换。实验结果表明，所提方法能够显著提高MLP算法在皮肤颜色识别问题上的性能。
📚2 运行结果
部分代码：
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 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 BestMSE=inf; BestMass=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); 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 end fitness=fitness/TrainingNO; CurrentFitness(i) = fitness; end best=min(CurrentFitness); worst=max(CurrentFitness);
🌈3 Matlab代码实现
🎉4 参考文献
部分理论来源于网络，如有侵权请联系删除。
@inproceedings{Mohseni2013TrainingFN, title={Training feedforward neural networks using hybrid particle swarm optimization and gravitational search}, author={Mahmood Mohseni and Mehdi Ramezani}, year={2013} }
