State Estimation of Memristor- Based Recurrent Neural Networks with TimeVarying Delays Based on Passivity Theory

Title
State Estimation of Memristor- Based Recurrent Neural Networks with TimeVarying Delays Based on Passivity Theory
Author(s)
박주현R. Rakkiyappan[R. Rakkiyappan]A. Chandrasekar[A. Chandrasekar]S. Lakshmanan[S. Lakshmanan]
Keywords
TIME-VARYING DELAYS; ASYMPTOTIC STABILITY; NONLINEAR-SYSTEMS; SYNCHRONIZATION
Issue Date
201404
Publisher
WILEY-BLACKWELL
Citation
COMPLEXITY, v.19, no.4, pp.32 - 43
Abstract
This article deals with the state estimation problem of memristor-based recurrent neural networks (MRNNs) with time-varying delay based on passivity theory. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delay, the dynamics of the estimation error is passive from the control input to the output error. Based on the Lyapunov-Krasovskii functional (LKF) involving proper triple integral terms, convex combination technique, and reciprocal convex technique, a delay-dependent state estimation of MRNNs with time-varying delay is established in terms of linear matrix inequalities (LMIs). The information about the neuron activation functions and lower bound of the time-varying delays is fully used in the LKF. Then, the desired estimator gain matrix is accomplished by solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed theoretical results. (c) 2013 Wiley Periodicals, Inc. Complexity 19: 32-43, 2014
URI
http://hdl.handle.net/YU.REPOSITORY/32573http://dx.doi.org/10.1002/cplx.21482
ISSN
1076-2787
Appears in Collections:
공과대학 > 전기공학과 > Articles
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