A new approach for estimating the attraction domain for Hopfield-type neural networks.
In this letter, using methods proposed by E. Kaslik, St. Balint, and their colleagues, we develop a new method, expansion approach, for estimating the attraction domain of asymptotically stable equilibrium points of Hopfield-type neural networks. We prove theoretically and demonstrate numerically that the proposed approach is feasible and efficient. The numerical results that obtained in the application examples, including the network system considered by E. Kaslik, L. Brăescu, and St. Balint, indicate that the proposed approach is able to achieve better attraction domain estimation.
Department of Mathematics, Faculty of Science, Xi'an Jiaotong University, Xi'an 710049, China. dqjin@yahoo.cn
Dequan
D
Jin
Jigen
J
Peng
D000465:Algorithms; D003198:Computer Simulation; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer