Detecting oscillations in neural networks via frequency domain analysis

Jorge L. Moiola, Daniel Berns, Guanrong Chen, Haluk Ogmen

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

In this paper we offer a graphical analysis for detecting orbital oscillations in a neural network model. The approach taken is based on a frequency domain methodology, in which graphical techniques are used to capture some of the intrinsic dynamical features of periodic solutions in a nonlinear neural network system with time delays. The graphical analysis provides a correct detection of large-amplitude and multiple limit cycles existing in the network model.
Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages669-673
Volume2
Publication statusPublished - 1997
Externally publishedYes
Event1997 IEEE International Conference on Neural Networks (ICNN'97) - Westin Galleria Hotel, Houston, Texas, United States
Duration: 9 Jun 199712 Jun 1997

Publication series

Name
Volume2

Conference

Conference1997 IEEE International Conference on Neural Networks (ICNN'97)
Abbreviated titleICNN'97
PlaceUnited States
CityHouston, Texas
Period9/06/9712/06/97

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