Title: Stability analysis of generalised Neural Networks with mixed time-varying delays
Authors: Yuanyuan Wu, Yuqiang Wu, Yonggang Chen
Addresses: School of Automation, Southeast University, Nanjing 210096, China. ' Research Institute of Automation, Qufu Normal University, Qufu 273165, China. ' Department of Mathematics, Henan Institute of Science and Technology, Xinxiang 453003, China
Abstract: For a class of Recurrent Neural Networks (RNNs) with mixed discrete and distributed delays, this paper is concerned with the problems of determining the global exponential stability and estimating the exponential convergence rate. By employing Lyapunov-Krasovskii functional approach, novel delay-dependent criteria are derived in term of LMIs, which can guarantee the global exponential stability of concerned systems, meanwhile, the exponential convergence rate can be estimated. Two numerical examples are given to show the effectiveness and improvement of the obtained results.
Keywords: delay dependent criteria; exponential stability; recurrent neural networks; RNNs; LMI; linear matrix inequality; stability analysis; time-varying delays; mixed delays.
DOI: 10.1504/IJSCC.2010.035417
International Journal of Systems, Control and Communications, 2010 Vol.2 No.4, pp.349 - 363
Published online: 30 Sep 2010 *
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