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 Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales | Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2009 Article ID 491268 17 pages doi 2009 491268 Research Article Global Exponential Stability of Delayed Cohen-Grossberg BAM Neural Networks with Impulses on Time Scales Yongkun Li 1 Yuchun Hua 1 and Yu Fei2 1 Department of Mathematics Yunnan University Kunming Yunnan 650091 China 2 School of Statistics and Mathematics Yunnan University of Finance and Economics Kunming Yunnan 650221 China Correspondence should be addressed to Yongkun Li yklie@ Received 18 April 2009 Accepted 14 July 2009 Recommended by Patricia J. Y. Wong 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 first time applying the time-scale calculus theory to unify the discretetime and continuous-time Cohen-Grossberg BAM neural network with impulses under the same framework. Copyright 2009 Yongkun Li et al. 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 In the recent years bidirectional associative memory BAM neural networks and Cohen-Grossberg neural networks CGNNs with their various generalizations have attracted the attention of many mathematicians physicists and computer scientists see 1-17 due to their wide range of applications in for example pattern recognition associative memory and combinatorial optimization. Particularly as discussed in 18-20 in the hardware implementation of the neural networks when communication and response of neurons happens time delays may occur. Actually time delays
