Delay-dependent H-infinity state estimation of neural networks with mixed time-varying delays

Title
Delay-dependent H-infinity state estimation of neural networks with mixed time-varying delays
Author(s)
박주현S. Lakshmanan[S. Lakshmanan]K. Mathiyalagan[K. Mathiyalagan]R. Sakthivel[R. Sakthivel]F.A. Rihan[F.A. Rihan]
Keywords
DISTRIBUTED DELAYS; NEUTRAL-TYPE; EXPONENTIAL STABILITY; COMPLEX NETWORKS; MARKOVIAN JUMP; DISCRETE; SYNCHRONIZATION; LEAKAGE; DESIGN; PARAMETERS
Issue Date
201404
Publisher
ELSEVIER SCIENCE BV
Citation
NEUROCOMPUTING, v.129, pp.392 - 400
Abstract
In this paper, the delay-dependent H-infinity, state estimation of neural networks with a mixed time-varying delay is considered. By constructing a suitable Lyapunov-Krasovskii functional with triple integral terms and using Jensen inequality and linear matrix inequality (LMI) framework, the delay-dependent criteria are presented so that the error system is globally asymptotically stable with H-infinity performance. The activation functions are assumed to satisfy sector-like nonlinearities. The estimator gain matrix for delayed neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. Finally a numerical example with simulation is presented to demonstrate the usefulness and effectiveness of the obtained results. (C) 2013 Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/32469http://dx.doi.org/10.1016/j.neucom.2013.09.020
ISSN
0925-2312
Appears in Collections:
공과대학 > 전기공학과 > Articles
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