BP网络实现函数逼近.doc

% 函数逼近 % BP逼近 % 正弦函数 p=[-1:.05:1]; t=sin(pi*p); plot(p,t,-) title(要逼近的非线性函数) xlabel(时间) ylabel(非线性函数) %建立网络 net=newff(minmax(p),[n,1],{tansig purelin},trainlm) ??? Undefined function or variable n. net=newff(minmax(p),[10,1],{tansig purelin},trainlm) net = Neural Network object: architecture: numInputs: 1 numLayers: 2 biasConnect: [1; 1] inputConnect: [1; 0] layerConnect: [0 0; 1 0] outputConnect: [0 1] targetConnect: [0 1] numOutputs: 1 (read-only) numTargets: 1 (read-only) numInputDelays: 0 (read-only) numLayerDelays: 0 (read-only) subobject structures: inputs: {1x1 cell} of inputs layers: {2x1 cell} of layers outputs: {1x2 cell} containing 1 output targets: {1x2 cell} containing 1 target biases: {2x1 cell} containing 2 biases inputWeights: {2x1 cell} containing 1 input weight layerWeights: {2x2 cell} containing 1 layer weight functions: adaptFcn: trains gradientFcn: calcjx initFcn: initlay performFcn: mse trainFcn: trainlm parameters: adaptParam: .passes gradientParam: (none) initParam: (none) performParam: (none) trainParam: .epochs, .goal, .max_fail, .mem_reduc, .min_grad, .mu, .mu_dec, .mu_inc, .mu_max, .show, .time weight and bias values: IW: {2x1 cell} containing 1 input weight matrix LW: {2x2 cell} containing 1 layer weight matrix b: {2x1 cell} containing 2 bias vectors other: userdata: (user information) y1=sim(net,p) y1 = Columns 1 through 3 -2.639884443340668 -3.003073712383436 -3.194033204190848 Columns 4 through 6 -3.334757182601289 -3.594283021881933 -3.952800035494339 Columns 7 through 9 -4.173755066624227 -4.229926693495091 -4.177140178354165 Columns 10 through 12