Title :
Globally Asymptotic Stability of a Class of Neutral-Type Neural Networks With Delays
Author :
Cheng, Chao-Jung ; Liao, Teh-Lu ; Yan, Jun-Juh ; Hwang, Chi-Chuan
Author_Institution :
Dept. of Inf. Eng., Kun Shan Univ. of Technol., Tainan
Abstract :
Several stability conditions for a class of systems with retarded-type delays are presented in the literature. However, no results have yet been presented for neural networks with neutral-type delays. Accordingly, this correspondence investigates the globally asymptotic stability of a class of neutral-type neural networks with delays. This class of systems includes Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks. Based on the Lyapunov stability method, two delay-independent sufficient stability conditions are derived. These stability conditions are easily checked and can be derived from the connection matrix and the network parameters without the requirement for any assumptions regarding the symmetry of the interconnections. Two illustrative examples are presented to demonstrate the validity of the proposed stability criteria
Keywords :
Hopfield neural nets; Lyapunov methods; asymptotic stability; cellular neural nets; delays; Cohen-Grossberg neural network; Hopfield neural network; Lyapunov stability method; asymptotic stability; cellular neural network; delays; neutral-type neural network; Asymptotic stability; Cellular neural networks; Delay effects; Delay systems; Hopfield neural networks; Integrated circuit interconnections; Lyapunov method; Neural networks; Stability criteria; Very large scale integration; Cellular neural networks (CNNs); Cohen–Grossberg neural networks (CGNNs); Hopfield neural networks (HNNs); neutral-type neural networks;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2006.874677
