Question

newrb()函数如何不显示training with newrb窗口

Answer 1

应该是运行完后，在命令窗口输入
net.b{1}
net.iw{1,1}
net.b{2}
net.lw{2,1}
你可以在命令窗口输入type newrbe,查看该函数里面的一些参数，把你需要的输出即可

Answer 2

应该是运行完后，在命令窗口输入
net.b{1}
net.iw{1,1}
net.b{2}
net.lw{2,1}
你可以在命令窗口输入type newrbe,查看该函数里面的一些参数，把你需要的输出即可

Answer 3

• ，目都为调皮u回电‘蜗牛 
  

Answer 4

运行一次网络，在comand的命令中打入你的网络名词，
如果是默认的就打 net
你可以看到很多东西，这个就是我的网络的参数
net
net =
Neural Network
name: 'NAR Neural Network'
efficiency: .cacheDelayedInputs, .flattenTime,
.memoryReduction
userdata: (your custom info)
dimensions:
numInputs: 1
numLayers: 2
numOutputs: 1
numInputDelays: 2
numLayerDelays: 0
numFeedbackDelays: 0
numWeightElements: 496
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: 'divideint'
divideParam: .trainRatio, .valRatio, .testRatio
divideMode: 'time'
initFcn: 'initlay'
performFcn: 'mse'
performParam: .regularization, .normalization, .squaredWeighting
plotFcns: {'plotperform', plottrainstate, ploterrhist,
plotregression, plotresponse, ploterrcorr}
plotParams: {1x6 cell array of 6 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,inputStates] = net(inputs,inputStates)