第一次运行时> 提示 function_name(load('c:\6.txt'),load('c:\5.txt'),'10000','1e-6');

??? Error using ==> function_name

Too many input arguments.

我将P,T两个参数去掉，将里面算法去掉一个，直接计算其中一个，可以算出一些值，不过MSE的值还是没法求出来，帮我看看是哪里出了问题，谢谢，下面是我改的，还有错误提示

function MSE = function_name(epochs, goal)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% NEWFF——生成一个新的前向神经网络

% TRAIN——对 BP 神经网络进行训练

% SIM——对 BP 神经网络进行仿真

% 定义训练样本

% P 为输入矢量

% fid 为文件名

P=load('c:\6.txt');

T=load('c:\5.txt');

P=P';

T=T';

[PN,minp,maxp,TN,mint,maxt] = premnmx(P,T)

% 对数据进行预处理，进行归一化处理

rand('state',0)

net=newff(minmax(PN),[15,1],{'tansig','purelin'})

% 创建一个新的前向神经网络

%clc

% disp('1. L-M 优化算法 TRAINLM');

% disp('2. 弹性梯度下降法 TRAINRP');

% choice=input('请选择训练算法(1,2):');

% figure(gcf);

% if(choice==1)

% 采用 L-M 优化算法 TRAINLM

net.trainFcn='trainlm';

% 设置训练参数

net.trainParam.epochs = epochs; %%%%%%%%%%%%%%%%%

net.trainParam.goal = goal; %%%%%%%%%%%%%%%%%%%%

net=init(net);

% 重新初始化

% elseif(choice==2)

% 采用弹性梯度下降法 TRAINRP

% net.trainFcn='trainrp';

% 设置训练参数

% net.trainParam.epochs = epochs; %%%%%%%%%%%%%%%%%%%%

% net.trainParam.goal = goal; %%%%%%%%%%%%%%%%%%%%%%%

% net = init(net);

% 重新初始化

% end

% 调用相应算法训练 BP 网络

[net,tr]=train(net,PN,TN);

inputWeights=net.IW{1,1}

inputbias=net.b{1}

layerWeights=net.LW{2,1}

layerbias=net.b{2}

% 对 BP 网络进行仿真

AN = sim(net,PN);

% 计算仿真误差

[A] = postmnmx(AN,mint,maxt);

% 恢复用premnmx处理过的数据

E = T - A;

MSE=mse(E);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end

这个事输入命令的 MSE=function_name('10000','1e-6');

以下是错误提示

Warning: PREMNMX is an obsolete function.

> In nntobsf at 18

In premnmx at 20

In function_name at 15

Use MAPMINMAX instead, type HELP PREMNMX for bug warning.

PN =

Columns 1 through 12

0.5327    1.0000    0.2570    0.4673   -0.6215   -0.0654    0.0537   -0.7033   -0.3925   -0.9533    0.2150    0.6402

0.8730    0.6825   -0.0476    1.0000    0.1429    0.4921    0.2063    0.1111   -0.2381   -1.0000    0.5238    0.9683

-0.0947   -0.8947    0.5579   -0.9158    0.1789   -0.5368   -0.3474   -0.9368    0.6211    1.0000   -1.0000   -1.0000

Columns 13 through 19

-1.0000    0.1308   -0.2780   -0.5561    0.2033    0.3598    0.5023

-0.8413    0.4603    0.3968   -0.1746    0.0159    0.0476    0.4286

0.5789   -0.1579   -0.8526   -0.3474   -0.3263    0.7474   -0.2211

minp =

10.6000

14.2000

61.0000

maxp =

19.1600

20.5000

156.0000

TN =

Columns 1 through 12

0.8751   -0.0831    0.6589   -0.1705   -0.0956   -0.0342   -0.1495   -1.0000    0.1162   -0.4775   -0.5833   -0.2269

Columns 13 through 19

-0.5137    0.2477   -0.6730   -0.6198   -0.1499    1.0000    0.3701

mint =

9.3408

maxt =

27.8640

Warning: NEWFF used in an obsolete way.

> In nntobsu at 18

In newff at 105

In function_name at 18

See help for NEWFF to update calls to the new argument list.

net =

Neural Network object:

architecture:

numInputs: 1

numLayers: 2

biasConnect: [1; 1]

inputConnect: [1; 0]

layerConnect: [0 0; 1 0]

outputConnect: [0 1]

numOutputs: 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

biases: {2x1 cell} containing 2 biases

inputWeights: {2x1 cell} containing 1 input weight

layerWeights: {2x2 cell} containing 1 layer weight

functions:

adaptFcn: 'trains'

divideFcn: (none)

gradientFcn: 'calcjx'

initFcn: 'initlay'

performFcn: 'mse'

trainFcn: 'trainlm'

parameters:

adaptParam: .passes

divideParam: (none)

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)

??? Error using ==> trainlm at 202

Epochs is not a positive integer.

Error in ==> network.train at 300

[net,tr,Ac,El] = feval(net.trainFcn,net,Pd,Tl,Ai,Q2,TS2,VV,TV);

Error in ==> function_name at 44

[net,tr]=train(net,PN,TN);