Abstract
This paper is concerned with the problem of stochastic stability for a class of fuzzy Cohen-Grossberg neural networks, in which the interconnections and delays are time-varying. Based on a Lyapunov function and the Ito differential formula, a set of novel sufficient conditions on the pth moment exponential stability of the equilibrium of the system is derived. Moreover, an illustrative example is given to demonstrate the effectiveness of the results obtained.
Highlights
Cohen and Grossberg neural networks [ ] have been extensively studied and applied in many different fields such as associative memory, signal processing, and some optimization problems
Due to the finite speeds of the switching and transmission of signals, time delays do exist in a working network and should be incorporated into the model equation [ – ]
Stochastic perturbations should be taken into account when modeling neural networks
Summary
Cohen and Grossberg neural networks [ ] have been extensively studied and applied in many different fields such as associative memory, signal processing, and some optimization problems. In such applications, it is of prime importance to ensure that the designed neural networks are stable [ ]. It has been realized that the synaptic transmission is a noisy process brought about by random fluctuations from the release of neurotransmitters and other probabilistic causes, and it is of great significance to consider stochastic effects on the stability of neural networks described by stochastic functional differential equations.
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