Global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays

Authors:
Boshan Chen;Jun Wang
Affiliations:
Dept. of Math., Hubei Normal Univ., China;-
Venue:
IEEE Transactions on Neural Networks
Year:
2005

Citing 0
Cited 3

Multiperiodicity of periodically oscillated discrete-time neural networks with transient excitatory self-connections and sigmoidal nonlinearities

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Global exponential convergence of periodic neural networks with time-varying delays

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Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays

Information Sciences: an International Journal

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Abstract

In this paper, we present the analytical results on the global exponential periodicity of a class of recurrent neural networks with oscillating parameters and time-varying delays. Sufficient conditions are derived for ascertaining the existence, uniqueness and global exponential periodicity of the oscillatory solution of such recurrent neural networks by using the comparison principle and mixed monotone operator method. The periodicity results extend or improve existing stability results for the class of recurrent neural networks with and without time delays.