tailieunhanh - Báo cáo hóa học: " Research Article Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms | Hindawi Publishing Corporation Advances in Difference Equations Volume 2007 Article ID 78160 18 pages doi 2007 78160 Research Article Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms Qiankun Song and Jinde Cao Received 9 March 2007 Accepted 16 May 2007 Recommended by Ulrich Krause Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence uniqueness and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory analytic methods and inequality techniques. Moreover the exponential convergence rate index is estimated which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results. Copyright 2007 Q. Song and J. Cao. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction The bidirectional associative memory BAM neural network model was first introduced by Kosko 1 . This class of neural networks has been successfully applied to pattern recognition signal and image processing artificial intelligence due to its generalization of the single-layer auto-associative Hebbian correlation to two-layer pattern-matched heteroas-sociative circuits. Some of these applications require that the designed network has a unique stable equilibrium point. In hardware implementation time delays occur due to finite switching speed of the amplifiers and communication time 2 . Time delays will affect the stability of designed neural networks and may lead to some complex dynamic behaviors such as periodic oscillation bifurcation or chaos 3 . .

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