Reliable stabilization for memristor-based recurrent neural networks with time-varying delays

Title
Reliable stabilization for memristor-based recurrent neural networks with time-varying delays
Author(s)
박주현칼리다스마디야라간R. Anbuvithya[R. Anbuvithya]R. Sakthivel[R. Sakthivel]P. Prakash[P. Prakash]
Keywords
VEHICLE SUSPENSION SYSTEMS; EXPONENTIAL STABILITY; DISSIPATIVE CONTROL; CRITERIA; FAULT
Issue Date
201504
Publisher
ELSEVIER SCIENCE BV
Citation
NEUROCOMPUTING, v.153, pp.140 - 147
Abstract
In this paper, a general class of memristive recurrent neural networks with time-varying delays is considered. Based on the knowledge of memristor and recurrent neural networks (RNNs), a model of memristive based RNNs is established. After that the problem of reliable stabilization is studied by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using linear matrix inequality (LMI) framework. By use of the Wirtinger-type inequality, sufficient conditions are presented for the existence of a reliable state feedback controller, which can guarantee the global asymptotic stability of the memristive RNNs. Finally, an example is given to illustrate the theoretical results via numerical simulations. (C) 2014 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/32726http://dx.doi.org/10.1016/j.neucom.2014.11.043
ISSN
0925-2312
Appears in Collections:
공과대학 > 전기공학과 > Articles
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