Global stability of complex-valued recurrent neural networks with time-delays

Jin Hu, Jun Wang

Research output: Contribution to journalArticlepeer-review

295 Citations (Scopus)

Abstract

Since the last decade, several complex-valued neural networks have been developed and applied in various research areas. As an extension of real-valued recurrent neural networks, complex-valued recurrent neural networks use complex-valued states, connection weights, or activation functions with much more complicated properties than real-valued ones. This paper presents several sufficient conditions derived to ascertain the existence of unique equilibrium, global asymptotic stability, and global exponential stability of delayed complex-valued recurrent neural networks with two classes of complex-valued activation functions. Simulation results of three numerical examples are also delineated to substantiate the effectiveness of the theoretical results.

Original languageEnglish
Article number6194338
Pages (from-to)853-865
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue number6
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Complex-valued neural network
  • global asymptotic stability
  • global exponential stability
  • neurodynamic analysis
  • time delays

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