tailieunhanh - Báo cáo hóa học: " Research Article Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay"

RTuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: esearch Article Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay | Hindawi Publishing Corporation Journal of Inequalities and Applications Volume 2009 Article ID 385298 20 pages doi 2009 385298 Research Article Global Exponential Stability of Periodic Oscillation for Nonautonomous BAM Neural Networks with Distributed Delay Yi Wang 1 Zhongjun Ma 2 3 and Hongli Liu1 1 School of Mathematics and Statistics Zhejiang University of Finance Economical Hangzhou 310012 China 2 School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin 541004 China 3 School of Mathematical Science and Computing Technology Central South University Changsha 410083 China Correspondence should be addressed to Yi Wang wangyihzh@ Received 22 March 2009 Revised 7 July 2009 Accepted 2 October 2009 Recommended by Alexander I. Domoshnitsky We derive a new criterion for checking the global stability of periodic oscillation of bidirectional associative memory BAM neural networks with periodic coefficients and distributed delay and find that the criterion relies on the Lipschitz constants of the signal transmission functions weights of the neural network and delay kernels. The proposed model transforms the original interacting network into matrix analysis problem which is easy to check thereby significantly reducing the computational complexity and making analysis of periodic oscillation for even large-scale networks. Copyright 2009 Yi Wang 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 The bidirectional associative memory BAM neural network which was first introduced by Kosko in 1987 1 2 is formed by neurons arranged in two layers. The neurons in one layer are fully interconnected to the neurons in the other layer while there are no interconnections among neurons in the same layer. Through iterations of forward and backward .