Abstract: The global exponential stability is investigated for a class of reaction-diffusion Cohen-Grossberg-type BAM neural networks with time delays. By constructing suitable Lyapunov functional and using homeomorphism mapping, several sufficient conditions guaranteeing the existence, uniqueness and global exponential stability of reaction-diffusion Cohen-Grossberg-type BAM neural networks wi...

In this paper, a generalized reaction-diffusion model of Cohen Grossberg neural networks with time-varying and distributed delays is investigated. By employing analytic methods, inequality technique and M -matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for Cohen-Grossberg neural networks with time-varying and di...

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at le...

An impulsive Cohen-Grossberg neural network with time-varying and S-type distributed delays and reaction-diffusion terms is considered. By using Hardy-Poincaré inequality instead of Hardy-Sobolev inequality or just the nonpositivity of the reaction-diffusion operators, under suitable conditions in terms of M-matrices which involve the reaction-diffusion coefficients and the dimension and size o...

Based on the theory of calculus on time scales, the homeomorphism theory, Lyapunov functional method, and some analysis techniques, sufficient conditions are obtained for the existence, uniqueness, and global exponential stability of the equilibrium point of Cohen-Grossberg bidirectional associative memory BAM neural networks with distributed delays and impulses on time scales. This is the firs...

This paper is concerned with finite-time synchronization for a class of delayed neural networks with Cohen–Grossberg type. Different from the existing related results, the time-delayed feedback strategy is utilized to investigate finite-time synchronization of delayed Cohen–Grossberg neural networks. By constructing Lyapunov functions and using differential inequalities, several new and effecti...

In this paper, the global asymptotic stability of second-order neutral type Cohen-Grossberg neural networks with time-varying delays are investigated. First, by choosing properly variable substitution the system is transformed to first-order differential equation. Second, some sufficient conditions which can ensure the existence and global asymptotic stability of equilibrium point for the syste...

Methods drawn from control theory { Generalised Predictive Control and self-tuning control { are brought together with methods drawn from Neural Networks { Local Model Networks and Modular Neural Networks { to give a multiple-model adaptive algorithm for the control of non-linear systems whose dynamics depend on an unknown external signal. The method overcomes the \stability-plasticity dilemma"...

In this brief, robust adaptive control of unknown modified Cohen–Grossberg neural networks with time delays is considered based on nonsmooth analysis and matrix inequality technique. Several new controllers are designed to ensure the global asymptotical stability of the targeted equilibrium point. The designed controllers are independent of the bounds of the perturbations, system functions and ...

This paper is concerned with analysis problem for the global exponential stability of the Cohen–Grossberg neural networks with discrete delays and with distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, we employ Lyapunov functions to establi...