我自己写的东西，但是目标和输出差距也太大了点，真心搞不懂啊，就是个凝汽器故障诊断，老师说老简单了，是我太笨了吧，我新手，求关注啊！求帮助，谢谢
p=[1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0;1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0;1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0;1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0;0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0;0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0;0 1 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0;0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0;0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0;0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0;0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1]
p =
1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0
1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0
1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0
1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0
0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
0 1 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0
0 1 0 0 0 1 0 1 0 1 0 0 0 1 1 0 0
0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0
0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0
0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0
0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1
>> t=[1 0 0 0 0 0 0 0 0 0 0;0 1 0 0 0 0 0 0 0 0 0;0 0 1 0 0 0 0 0 0 0 0;0 0 0 1 0 0 0 0 0 0 0;0 0 0 0 1 0 0 0 0 0 0;0 0 0 0 0 1 0 0 0 0 0;0 0 0 0 0 0 1 0 0 0 0;0 0 0 0 0 0 0 1 0 0 0;0 0 0 0 0 0 0 0 1 0 0;0 0 0 0 0 0 0 0 0 1 0;0 0 0 0 0 0 0 0 0 0 1]
t =
1 0 0 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0
0 0 0 1 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 0 0 0 0 0
0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 0 1 0 0
0 0 0 0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0 0 0 1
>> p=p';
>> t=t';
>> net=newff(p,t,{10},{'tansig','tansig'},'trainlm');
>> net.trainParam.goal=0.003;
>> net.trainParam.epochs=5000;
>> [net,tr]=train(net,p,t)
net =
Neural Network
name: 'Custom Neural Network'
efficiency: .cacheDelayedInputs, .flattenTime,
.memoryReduction, .flattenedTime
userdata: (your custom info)
dimensions:
numInputs: 1
numLayers: 2
numOutputs: 1
numInputDelays: 0
numLayerDelays: 0
numFeedbackDelays: 0
numWeightElements: 301
sampleTime: 1
connections:
biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]
subobjects:
inputs: {1x1 cell array of 1 input}
layers: {2x1 cell array of 2 layers}
outputs: {1x2 cell array of 1 output}
biases: {2x1 cell array of 2 biases}
inputWeights: {2x1 cell array of 1 weight}
layerWeights: {2x2 cell array of 1 weight}
functions:
adaptFcn: 'adaptwb'
adaptParam: (none)
derivFcn: 'defaultderiv'
divideFcn: 'dividerand'
divideParam: .trainRatio, .valRatio, .testRatio
divideMode: 'sample'
initFcn: 'initlay'
performFcn: 'mse'
performParam: .regularization, .normalization
plotFcns: {'plotperform', plottrainstate,
plotregression}
plotParams: {1x3 cell array of 3 params}
trainFcn: 'trainlm'
trainParam: .showWindow, .showCommandLine, .show, .epochs,
.time, .goal, .min_grad, .max_fail, .mu, .mu_dec,
.mu_inc, .mu_max
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
methods:
adapt: Learn while in continuous use
configure: Configure inputs & outputs
gensim: Generate Simulink model
init: Initialize weights & biases
perform: Calculate performance
sim: Evaluate network outputs given inputs
train: Train network with examples
view: View diagram
unconfigure: Unconfigure inputs & outputs
evaluate: outputs = net(inputs)
tr =
trainFcn: 'trainlm'
trainParam: [1x1 struct]
performFcn: 'mse'
performParam: [1x1 struct]
derivFcn: 'defaultderiv'
divideFcn: 'dividerand'
divideMode: 'sample'
divideParam: [1x1 struct]
trainInd: [3 4 7 8 9 10 11]
valInd: [1 2]
testInd: [5 6]
stop: 'Performance goal met.'
num_epochs: 5
trainMask: {[11x11 double]}
valMask: {[11x11 double]}
testMask: {[11x11 double]}
best_epoch: 3
goal: 0.0030
states: {'epoch' 'time' 'perf' 'vperf' 'tperf' 'mu' 'gradient' 'val_fail'}
epoch: [0 1 2 3 4 5]
time: [0.2610 0.3960 0.4200 0.4560 0.4740 0.4940]
perf: [0.5254 0.2044 0.1309 0.0548 0.0362 0.0028]
vperf: [0.3965 0.1823 0.2113 0.1067 0.1128 0.1507]
tperf: [0.5361 0.2193 0.1852 0.1515 0.1599 0.1454]
mu: [1.0000e-03 1.0000e-04 1.0000e-05 1.0000e-04 1.0000e-05 1.0000e-06]
gradient: [0.1669 0.1778 0.2073 0.0977 0.1404 0.0221]
val_fail: [0 0 1 0 1 2]
best_perf: 0.0548
best_vperf: 0.1067
best_tperf: 0.1515
>> a=sim(net,p)
a =
Columns 1 through 8
0.0013 0.0051 0.0045 0.0015 0.0715 0.0049 0.0213 0.0061
0.0166 0.0020 0.0000 0.0162 0.0182 0.0014 0.0585 0.2230
0.0028 0.0008 0.9517 0.0128 0.0020 0.0786 0.0384 0.0019
0.5422 0.0063 0.0959 0.2451 0.0036 0.0002 0.1700 0.0452
0.0163 0.0018 0.0806 0.0290 0.0041 0.0190 0.0069 0.0727
0.0145 0.0205 0.0625 0.1302 0.0261 0.0186 0.0421 0.1343
0.1057 0.0064 0.0484 0.0828 0.0254 0.0115 0.8728 0.6152
0.0722 0.0039 0.0013 0.0021 0.1279 0.0011 0.5016 0.9676
0.0072 0.0062 0.0438 0.0679 0.6085 0.2168 0.9540 0.9995
0.2047 0.0338 0.0695 0.0140 0.1626 0.0000 0.0356 0.2438
0.0000 0.0053 0.0404 0.0366 0.0352 0.9510 0.0079 0.1147
Columns 9 through 11
0.0179 0.0465 0.0152
0.0624 0.0044 0.0029
0.0247 0.0006 0.0424
0.0645 0.0005 0.0003
0.0520 0.0019 0.0209
0.0285 0.0044 0.0237
0.2399 0.0094 0.0061
0.2872 0.2565 0.0020
0.9618 0.5802 0.1597
0.2327 0.5602 0.0000
0.0070 0.0017 0.9176
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