Proof of hidden node number in MLP and experiments on well log data inversion

Kou-Yuan Huang, Liang Chi Shen, Jiun Der You

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In the multilayer perceptron (MLP), there was a theorem about the maximum number of separable regions (M) given the number of hidden nodes (H) in the input d-dimensional space. We propose a recurrence relation in the high dimensional space and prove the theorem using the expansion of recurrence relation instead of proof by induction. The MLP model has input layer, one hidden layer, and output layer. We use different MLP models on the well log data inversion to test the number of hidden nodes determined by the theorem. The inputs are the first order, second order, and third order features. Higher order neural network (HONN) has the property of more nonlinear mapping. In the experiments, we have 31 simulated well log data. 25 well log data are used for training, and 6 are for testing. The experimental results can support the number of hidden nodes determined by the theorem.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
DOIs
StatePublished - 28 Sep 2015
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2015-September

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period12/07/1517/07/15

Keywords

  • Multilayer perceptron
  • hidden node number
  • higher order neural network
  • recurrence formula
  • well log data inversion

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