An analysis of stability of a class of neutral-type neural networks with discrete time delays

The problem of existence, uniqueness, and global asymptotic stability is considered for the class of neutral-type neural network model with discrete time delays. By employing a suitable Lyapunov functional and using the homeomorphism mapping theorem, we derive some new delay-independent sufficient conditions for the existence, uniqueness, and global asymptotic stability of the equilibrium point for this class of neutral-type systems. The obtained conditions basically establish some norm and matrix inequalities involving the network parameters of the neural system. The main advantage of the proposed results is that they can be expressed in terms of network parameters only. Some comparative examples are also given to compare our results with the previous corresponding results and demonstrate the effectiveness of the results presented.Publisher's Versio

New sufficient conditions for global stability of neutral-type neural networks with time delays

This paper studies the equilibrium and stability properties of the class of neutral-type neural network model with discrete time delays. By employing a Lyapunov functional and examining the time derivative of the Lyapunov functional, we obtain some delay independent sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point for this class of neutral-type systems. The obtained conditions can be easily verified as they can be expressed in terms of the network parameters only. We also compare our results with the previous corresponding results derived in the literature by giving some numerical examples. (C) 2012 Elsevier B.V. All rights reserved

An improved stability result for delayed Takagi-Sugeno fuzzy Cohen-Grossberg neural networks

This work proposes a novel and improved delay independent global asymptotic stability criterion for delayed Takagi-Sugeno (T-S) fuzzy Cohen-Grossberg neural networks exploiting a suitable fuzzy-type Lyapunov functional in the presence of the nondecreasing activation functions having bounded slopes. The proposed stability criterion can be easily validated as it is completely expressed in terms of the system matrices of the fuzzy neural network model considered. It will be shown that the stability criterion obtained in this work for this type of fuzzy neural networks improves and generalizes some of the previously published stability results. A constructive numerical example is also given to support the proposed theoretical results. (c) 2018 Elsevier Ltd. All rights reserved

New results for global stability of Cohen-Grossberg neural networks with discrete time delays

This paper studies the global convergence properties of Cohen-Grossberg neural networks with discrete time delays. Without assuming the symmetry of interconnection weight coefficients, and the monotonicity and differentiability of activation functions, and by employing Lyapunov functionals, we derive new delay independent sufficient conditions under which a delayed Cohen-Grossberg neural network converges to a globally asymptotically stable equilibrium point. Some examples are given to illustrate the advantages of the results over the previously reported results in the literature

An Analysis of Global Stability of Cohen-Grossberg Neural Networks with Multiple Time Delays

This paper presents a new sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for Cohen-Grossberg neural networks with multiple time delays. The results establish a relationship between the network parameters of the neural system independently of the delay parameters. The results are also compared with the previously reported results in the literature

Global stability analysis of Cohen-Grossberg neural networks with time varying delays

This Letter presents some sufficient conditions for the existence, uniqueness and global asymptotic and exponential stability of the equilibrium point for Cohen-Grossberg neural networks with time delays. The results establish a relationship between the network parameters of the neural system independently of the delay parameters. The results are also compared with the previously reported results in the literature. (c) 2005 Elsevier B.V. All rights reserved

An Analysis of Stability of a Class of Neutral-Type Neural Networks with Discrete Time Delays

The problem of existence, uniqueness, and global asymptotic stability is considered for the class of neutral-type neural network model with discrete time delays. By employing a suitable Lyapunov functional and using the homeomorphism mapping theorem, we derive some new delay-independent sufficient conditions for the existence, uniqueness, and global asymptotic stability of the equilibrium point for this class of neutral-type systems. The obtained conditions basically establish some norm and matrix inequalities involving the network parameters of the neural system. The main advantage of the proposed results is that they can be expressed in terms of network parameters only. Some comparative examples are also given to compare our results with the previous corresponding results and demonstrate the effectiveness of the results presented