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Báo cáo hóa học: " Existence and globally exponential stability of equilibrium for fuzzy BAM neural networks with distributed delays and impulse"

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: Existence and globally exponential stability of equilibrium for fuzzy BAM neural networks with distributed delays and impulse | Zhang et al. Advances in Difference Equations 2011 2011 8 http www.advancesindifferenceequations.eom content 2011 1 8 o Advances in Difference Equations a SpringerOpen Journal RESEARCH Open Access Existence and globally exponential stability of equilibrium for fuzzy BAM neural networks with distributed delays and impulse Qianhong Zhang1 2 Lihui Yang3 and Daixi Liao4 Correspondence zqianhong68@163.com 1Guizhou Key Laboratory of Economic System Simulation Guizhou College of Finance and Economics Guiyang Guizhou 550004 P.R.China Full list of author information is available at the end of the article Abstract In this article fuzzy bi-directional associative memory neural networks with distributed delays and impulses are considered. Some sufficient conditions for the existence and globally exponential stability of unique equilibrium point are established using fixed point theorem and differential inequality techniques. The results obtained are easily checked to guarantee the existence uniqueness and globally exponential stability of equilibrium point. MsC 34K20 34K13 92B20 Keywords Fuzzy BAM neural networks Equilibrium point Globally exponential stability Distributed delays Impulse SpringerOpen0 Introduction The bidirectional associative memory neural networks BAM models were first introduced by Kosko 1 2 . It is a special class of recurrent neural networks that can store bipolar vector pairs. The BAM neural network is composed of neurons arranged in two layers the X-layer and Y-layer. 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 information flows between the two layers it performs two-way associative search for stored bipolar vector pairs and generalize the single-layer autoassociative Hebbian correlation to two-layer pattern-matched heteroassociative circuits. Therefore this class of networks possesses a good .