Volume 2, Issue 4, December 2017, Page: 119-124
Global Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Impulsive Finite
Xuan Guo, School of Business Administration, Southwestern University of Finance and Economics, Chengdu, China
Hong Zhang, School of Information, Beijing Wuzi University, Beijing, China; Chinese Academy of Sciences, Bioinformatics Research Center, Beijing, China; Chinese Academy of Sciences, Power System Research Center, Beijing, China; Chinese Academy of Sciences, Partial Differential Equation and Its Application Center, Beijing, China; Chinese Academy of Sciences, Statistical Science Research Center, Beijing, China; Chinese Academy of Sciences, Center for Optimization and Applied Research, Beijing, China; Chinese
Shanzai Lee, School of Information, Beijing Wuzi University, Beijing, China; Chinese Academy of Sciences, Bioinformatics Research Center, Beijing, China; Chinese Academy of Sciences, Power System Research Center, Beijing, China; Chinese Academy of Sciences, Partial Differential Equation and Its Application Center, Beijing, China; Chinese Academy of Sciences, Statistical Science Research Center, Beijing, China; Chinese Academy of Sciences, Center for Optimization and Applied Research, Beijing, China; Chinese
Received: Feb. 7, 2017;       Accepted: May 22, 2017;       Published: Jul. 17, 2017
DOI: 10.11648/j.mlr.20170204.12      View  1574      Downloads  72
Abstract
In this paper, we consider the sufficient conditions for the stability of periodic solutions of static recurrent neural networks with impulsive delay. In this paper, we study the time - delay static recurrent neural network affected by pulse. The results show that the neural network is stable when the pulse function is linear and relatively small, and a condition for the periodic solution with exponential stability is obtained. This paper introduces the research status of artificial neural network, summarizes the research background and development of static recurrent neural network dynamic system, and introduces the main work of this paper. Using the fixed point theory, M. The existence of periodic solutions and the global robust exponential stability of the static recursive neural network with variable delays and the existence of almost periodic solutions of the static recursive neural network of the partitioned time are studied by combining the properties of the matrix and the Lyapunov function combined with the inequality technique. Global exponential stability, the stability conditions of the corresponding problem are obtained respectively, and the results of the related research are generalized. Using Lyapunov. The stability of the quasi - static neural recursive neural network and the stability of the periodic solution are studied. The condition of the stationary static recursive neural network is obtained and the correctness of the condition is illustrated. Considering the influence of stochastic perturbation on the dynamic behavior of static recurrent neural network, the static recursive neural network with time delay and the static recursive neural network with distributed time delay are studied by using the infinitesimal operator, Ito formula and the convergence theorem of martingales. Global critical exponential stability of quasi - static neural network with stochastic perturbation. The static recursive neural network with Markovian modulation and the time-delay static recurrent neural network model considering both random perturbation and Markovian switching are studied. The linear matrix inequality, the finite state space Markov chain property and the Lyapunov-krasovskii function, The judgment condition of the global exponential stability of the system is obtained. Firstly, the global exponential stability problem of quasi - static neural neural network with time - delay and recursive neural network is studied by using the generalized Halanay inequality. Then the stability of the Markovian response sporadic static recurrent neural network is studied by combining the properties of Markov chain.
Keywords
Global Exponential Stability, Static Recurrent Neural Networks, Periodic Solutions, Impulsive Finite
To cite this article
Xuan Guo, Hong Zhang, Shanzai Lee, Global Exponential Stability of Periodic Solutions for Static Recurrent Neural Networks with Impulsive Finite, Machine Learning Research. Vol. 2, No. 4, 2017, pp. 119-124. doi: 10.11648/j.mlr.20170204.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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