Iteration determine in Self-Organizing Map to Cluster Data

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ahmad karim
ahmad karim 2017-6-29
评论： Qichen Deng 2022-4-6
in the attach i share an example of Self-Organizing Map that provided by MATLAB my problem is that the iteration number is fixed in 200. change it with other value like 150, 100, 500..
Regards
  1 个评论
Qichen Deng
Qichen Deng 2022-4-6
Could it be net.trainParam.epochs?
"epochs is maximum number of training iterations before training is stopped."
net.trainParam.epochs = some number
will change the number of training iterations

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采纳的回答

Greg Heath
Greg Heath 2017-6-29
Type, without ending semicolon
net = net
then all properties will be displayed.
Chose the one that corresponds to iteration number.
Then change it.
Something along the lines of
net.property.iteration number = 100.
to change 200 to 100.
Hope this helps.
Thank you for formally accepting my answer
Greg

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ahmad karim
ahmad karim 2017-6-29
Thanks alot sir, but when i type net=net. the informations that listed below are displayed bu there is not any information about iterartion. net =
Neural Network
name: 'Self-Organizing Map'
userdata: (your custom info)
dimensions:
numInputs: 1
numLayers: 1
numOutputs: 1
numInputDelays: 0
numLayerDelays: 0
numFeedbackDelays: 0
numWeightElements: 4800
sampleTime: 1
connections:
biasConnect: false
inputConnect: true
layerConnect: false
outputConnect: true
subobjects:
input: Equivalent to inputs{1}
output: Equivalent to outputs{1}
inputs: {1x1 cell array of 1 input}
layers: {1x1 cell array of 1 layer}
outputs: {1x1 cell array of 1 output}
biases: {1x1 cell array of 0 biases}
inputWeights: {1x1 cell array of 1 weight}
layerWeights: {1x1 cell array of 0 weights}
functions:
adaptFcn: 'adaptwb'
adaptParam: (none)
derivFcn: 'defaultderiv'
divideFcn: (none)
divideParam: (none)
divideMode: 'sample'
initFcn: 'initlay'
performFcn: 'mse'
performParam: .regularization, .normalization
plotFcns: {'plotsomtop', plotsomnc, plotsomnd,
plotsomplanes, plotsomhits, plotsompos}
plotParams: {1x6 cell array of 6 params}
trainFcn: 'trainbu'
trainParam: .showWindow, .showCommandLine, .show, .epochs,
.time
weight and bias values:
IW: {1x1 cell} containing 1 input weight matrix
LW: {1x1 cell} containing 0 layer weight matrices
b: {1x1 cell} containing 0 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

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