Using a wiener-type recurrent neural network with the minimum description length principle for dynamic system identification

Jeen Shing Wang, Hung Yi Lin, Yu Liang Hsu, Ya Ting Yang

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

Abstract

This paper presents a novel Wiener-type recurrent neural network with the minimum description length (MDL) principle for unknown dynamic nonlinear system identification. The proposed Wiener-type recurrent network resembles the conventional Wiener model that consists of a dynamic linear subsystem cascaded with a static nonlinear subsystem. The novelties of our approach include: 1) the realization of a conventional Wiener model into a simple connectionist recurrent network whose output can be expressed by a nonlinear transformation of a linear state-space equation; 2) the state-space equation mapped from the network topology can be used to analyze the characteristics of the network using the well-developed theory of linear systems; and 3) the overall network structure can be determined by the MDL principle effectively using only the input-output measurements. Computer simulations and comparisons with some existing recurrent networks have successfully confirmed the effectiveness and superiority of the proposed Wiener-type network with the MDL principle.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Artificial Intelligence - Third International Conference on Intelligent Computing, ICIC 2007, Proceedings
PublisherSpringer Verlag
Pages192-201
Number of pages10
ISBN (Print)9783540742012
DOIs
Publication statusPublished - 2007 Jan 1
Event3rd International Conference on Intelligent Computing, ICIC 2007 - Qingdao, China
Duration: 2007 Aug 212007 Aug 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4682 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Intelligent Computing, ICIC 2007
CountryChina
CityQingdao
Period07-08-2107-08-24

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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