On global exponential periodicity of dynamical neural systems

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Abstract

Exponential periodicity of continuous-time neural networks with delays is investigated. Without assuming the boundedness and differentiability of the activation functions, some new sufficient conditions ensuring existence and uniqueness of periodic solution for a general class of neural systems are obtained. Discrete-time analogue of the continuous- time system with periodic input is formulated and we study their dynamical characteristiccs. The exponential periodicity of the continuous- time system is preserved by the discrete-time analogue without any restriction imposed on the uniform discretization step-size.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages827-830
Number of pages4
DOIs
StatePublished - 2004
Externally publishedYes
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 25 Jul 200429 Jul 2004

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
Volume2
ISSN (Print)1098-7576

Conference

Conference2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/TerritoryHungary
CityBudapest
Period25/07/0429/07/04

Keywords

  • Delays
  • Discrete-time anal- ogue
  • Exponential periodicity

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