How to choose "Nlocmin,Nlocmax" in non linear regression and curve fitting?

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Kishore 2015-3-25

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/185277-how-to-choose-nlocmin-nlocmax-in-non-linear-regression-and-curve-fitting

评论： Greg Heath 2015-4-4

How can one choose "Nlocmin and Nlocmax" values for non linear regression using ANN feedforwardnet in the code mentioned by Greg sir??

Link to the thread

http://in.mathworks.com/matlabcentral/newsreader/view_thread/333427

2. Estimate, by trial and error, the minimum number of hidden nodes necessary for successfully approximating the underlying input-output transformation. For a smooth function with Nlocmin local minima and Nlocmax local maxima, a reasonable lower
bound is H >= 2*max(Nlocmin,Nlocmax). Sometimes it is prudent to count endpoint extrema as 1/2 a local extremum. The addition of real-world noise, interference and measurement error should not change that minimum number. However, the contamination may make it difficult to identify the significant error-free extrema.

Thanks

Kishore

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Answer 1

Greg Heath 2015-3-28

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https://ww2.mathworks.cn/matlabcentral/answers/185277-how-to-choose-nlocmin-nlocmax-in-non-linear-regression-and-curve-fitting#answer_173030

编辑：Greg Heath 2015-3-28

在 MATLAB Online 中打开

Plot the target and count the number of local extrema.

This will only work for relatively smooth functions.

Otherwise use the search over

Hmin:dH:Hmax < Hub

with a preference for Hmax << Hub

For details, search NEWSGROUP and ANSWERS using

greg Hub

Hope this helps

Thank you for formally accepting my answer

Greg

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Kishore 2015-3-30

编辑：Kishore 2015-3-30

在 MATLAB Online 中打开

1) What should be the criteria for selecting number of hidden neurons

Hub ?? or Ndof ??

2) I put 2 for loops in my code one for varying number of neurons and one for Ntrails, i ran the code for until i get the Ndof as negative quantity..

i get this result

 R2 values for H = 0 to 3 and Ntrails = 1 to 10
753160684721697  0.815846821332570 0.935058715861745  0.954252137206118
753160684721697  0.815846821332570 0.935884289937718  0.980321755267376
753160684721697  0.815846821332570 0.917201138717953  0.997488719109723
753160684721697  0.815846821332570 0.930553054754834  0.960149651411815
753160684721697  0.762563239630275 0.951581078215140  0.993348125338601
753160684721697  0.815846821332570 0.900031911479721  0.979790825957402
753160684721697  0.815846821332570 0.885977723609531  0.998753726801378
753160684721697  0.815846821332570 0.895716885586277  0.999055291675377
753160684721697  0.815846821332570 0.931318496839154  0.960757183796528
753160684721697  0.779425009498547 0.918015150082236  0.947511177559573