Application of prony signal analysis to recurrent neural networks

Mohammad Farrokhi, Can Isik

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

Abstract

Recurrent neural networks are highly nonlinear dynamic systems, and therefore it is not an easy task to analyze their dynamic behavior. The primary goal of this paper is to apply Prony signal analysis and a modified root locus technique to recurrent neural networks. Prony method is compared with other linearization methods to analyze recurrent neural networks, and their performances are demonstrated using simulated examples.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE Computer Society
Pages413-418
Number of pages6
Volume1
StatePublished - 1994
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period6/27/946/29/94

ASJC Scopus subject areas

  • Software

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  • Cite this

    Farrokhi, M., & Isik, C. (1994). Application of prony signal analysis to recurrent neural networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 1, pp. 413-418). IEEE Computer Society.