Statistical approximation learning of discontinuous functions using simultaneous recurrent neural networks

Masao Sakai, Noriyasu Homma, Madan M. Gupta, Kenichi Abe

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

Abstract

In this paper, we develop an architecture for a novel type of neural network which is known as simultaneous recurrent neural networks (SRNNs). Using this novel neural architecture, we propose a statistical approximation learning (SAL) method. The SRNNs have the capability to approximate non-smooth functions which cannot be approximated by using conventional multi-layer perceptrons (MLPs). However, the most of the learning methods for the SRNNs are computationally expensive due to their inherent recursive calculations. To solve this problem, as an approximation learning method, SAL method is proposed by using a statistical relation between the time-series of the network outputs and the network configuration parameters. Simulation results show that SRNN's trained by the proposed SAL method can learn a strongly nonlinear function efficiently within a practical computation time.

Original languageEnglish
Title of host publicationIEEE International Symposium on Intelligent Control - Proceedings
Pages434-439
Number of pages6
Publication statusPublished - 2002
EventProceedings of the 2002 IEEE International Symposium on Intelligent Control - Vancouver, Canada
Duration: 2002 Oct 272002 Oct 30

Other

OtherProceedings of the 2002 IEEE International Symposium on Intelligent Control
CountryCanada
CityVancouver
Period02/10/2702/10/30

Keywords

  • Back-propagation
  • Dynamic modelling
  • Learning algorithms
  • Neural networks and statistical approximation

ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

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  • Cite this

    Sakai, M., Homma, N., Gupta, M. M., & Abe, K. (2002). Statistical approximation learning of discontinuous functions using simultaneous recurrent neural networks. In IEEE International Symposium on Intelligent Control - Proceedings (pp. 434-439)