简洁的BP及RBF神经网络代码

BP神经网络

function [W,err]=BPTrain(data,label,hiddenlayers,nodes,type)%Train the bp artial nueral net work%input data,label,layers,nodes,type%data：dim*n%label:1*n%layers:m:number of hidden layers%nodes:num_1;num_2...num_m%type==1:create and train%type==0:train%tanh / 双曲正切： tanh(x) = sinh(x) / cosh(x)=[e^x - e^(-x)] / [e^x + e^(-x)]%(tanh(x))'=sech^2(x)%sech / 双曲正割： sech(x) = 1 / cosh(x) = 2 / [e^x + e^(-x)]if type==1   %create the nureal network and train   nodes=[size(data,1);nodes];   nodes=[nodes+1;size(label,1)];   %W{1}=random(,nodes(1));   layers=hiddenlayers+2;   for i=1:layers-2       W{i}=rand(nodes(i),nodes(i+1)-1);   end   W{layers-1}=rand(nodes(layers-1),nodes(layers));else    %do nothingend%train the bp network%the termination condition%iteration.erroriter=0;error=inf;maxiter=2000;lr=0.1;epision=0.1;ticwhile iter<maxiter&&error>epision    iter=iter+1;    error=0;    for k=1:size(data,2)        %forward process        y{1}=[data(:,k)];        v{1}=y{1};        for i=1:layers-1            y{i}=[1;y{i}];             v{i+1}=W{i}'*y{i};            y{i+1}=tanh(v{i+1});        end        %back process        error=error+abs(label(k)-y{layers});        delta=(label(k)-y{layers}).*((sech(v{layers}).^2));        W{layers-1}=W{layers-1}+lr.*(y{layers-1}*delta);        for i=layers-1:-1:2                 delta=sech(v{i}).^2.*(W{i}(1:size(W{i},1)-1,:)*delta);                 W{i-1}=W{i-1}+lr.*(y{i-1}*delta');        end    end     err(iter)=error;    errorendtoc

测试代码

function res=BPTest(W,data)for k=1:size(data,2)   y=data(:,k);    for i=1:length(W)-1       y=[1;y];       y=tanh((W{i}'*y));   end   res(k)=tanh(W{i+1}'*[1;y]);end

global rbf_sigma;global rbf_center;global rbf_weight;if strcmp(traintype,'data')    traindist=pdist2(traindata,traindata);    rbf_sigma=max(max(traindist))/(scale.^2);%/(2*sqrt(sqrt(length(traindata))));    rbf_center=traindata;    Phi=exp(-traindist./rbf_sigma);    rbf_weight=inv(Phi)*trainlabel;    else if strcmp(traintype,'cluster')        [Idx,C,sumD,D]=kmeans(traindata,K,'emptyaction','singleton');        traindist=pdist2(traindata,C);        Cdist=pdist2(C,C);        rbf_sigma=max(max(Cdist))/(scale.^2);%/(2*sqrt(sqrt(length(traindata))));        rbf_center=C;        Phi=exp(-traindist./rbf_sigma);        rbf_weight=inv(Phi'*Phi)*Phi'*trainlabel;    else if strcmp(traintype,'descend')                                end            endend

测试 代码

function predcict=RBFTest(data)global rbf_sigma;global rbf_center;global rbf_weight;testdist=pdist2(data,rbf_center);predcict=exp(-testdist./(2*rbf_sigma))*rbf_weight;