A statistical approximation learning method for simultaneous recurrent networks

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

Abstract

In this paper, a statistical approximation learning (SAL) method is proposed for a new type of neural networks, simultaneous recurrent networks (SRNs). The SRNs 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 SRNs are computationally expensive due to their inherent recursive calculations. To solve this problem, a novel approximation learning 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 the proposed method can learn a strongly nonlinear function efficiently.

Original languageEnglish
Title of host publicationIFAC Proceedings Volumes (IFAC-PapersOnline)
EditorsGabriel Ferrate, Eduardo F. Camacho, Luis Basanez, Juan. A. de la Puente
PublisherIFAC Secretariat
Pages409-414
Number of pages6
Edition1
ISBN (Print)9783902661746
DOIs
Publication statusPublished - 2002
Event15th World Congress of the International Federation of Automatic Control, 2002 - Barcelona, Spain
Duration: 2002 Jul 212002 Jul 26

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
Number1
Volume15
ISSN (Print)1474-6670

Other

Other15th World Congress of the International Federation of Automatic Control, 2002
CountrySpain
CityBarcelona
Period02/7/2102/7/26

Keywords

  • Backpropagation
  • Dynamic modelling
  • Learning algorithms
  • Model approximation
  • Neural networks and statistical approximation

ASJC Scopus subject areas

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'A statistical approximation learning method for simultaneous recurrent networks'. Together they form a unique fingerprint.

  • Cite this

    Sakai, M., Homma, N., & Abe, K. (2002). A statistical approximation learning method for simultaneous recurrent networks. In G. Ferrate, E. F. Camacho, L. Basanez, & J. A. de la Puente (Eds.), IFAC Proceedings Volumes (IFAC-PapersOnline) (1 ed., pp. 409-414). (IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 15, No. 1). IFAC Secretariat. https://doi.org/10.3182/20020721-6-es-1901.00721