shenjingwangluo.rar_SOM_som—bp

%神经网络的使用~~可用~ P=[-1 -1 2 2;0 5 0 5]; %被分类的对象数据 T=[-1 -1 1 1]; %描述分类结果 rang=[-1 2;0 5]; %描述输入向量的最大值和最小值 net=newff(rang,[3,1],{'tansig','logsig'},'trainlm'); %tansig输出范围是[-1 1] logsig输出范围是[0 1] %在三层网络中，隐含层神经元个数n2和输入层神经元个数n1关系约为 n2=2*n1+1 %net=newff(PR,[S1 S2...SNl],{TF1 TF2...TFNl},BTF,BLF,PF) takes, %PR -- R x 2 matrix of min and max values for R input elements %Si -- Size of ith layer, for Nl layers %TFi -- Transfer function of ith layer, default = 'tansig' %TF -- Backpropagation network training function, default = 'traingdx' %BLF -- Backpropagation weight/bias learning function, default = 'learngdm' %PF -- Performance function, default = 'mse' net.trainParam.epochs=1000; net.trainParam.goal=0.01; LP.lr=0.1; net=train(net,P,T); P_test=[2 4]'; Y=sim(net,P_test)