Exponential periodicity and stability of delayed neural networks

Authors:
Changyin Sun;Chun-Bo Feng
Affiliations:
College of Electric Engineering, Hohai University, Nanjing 210098, PR China and Research Institute of Automation, Southeast University, Nanjing 210096, PR China;Research Institute of Automation, Southeast University, Nanjing 210096, PR China
Venue:
Mathematics and Computers in Simulation
Year:
2004

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Abstract

In this paper, exponential periodicity and stability of delayed neural networks 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. The delayed Hopfield network, bidirectional associative memory network, and cellular neural network are special cases of the neural system model considered.