H∞ state estimation for discrete-time stochastic memristive BAM neural networks with mixed time-delays View Full Text


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Article Info

DATE

2019-04

AUTHORS

Zidong Wang, Hongjian Liu, Bo Shen, Fuad E. Alsaadi, Abdullah M. Dobaie

ABSTRACT

In this paper, the H∞ state estimation problem is investigated for a class of discrete-time stochastic memristive bidirectional associative memory (DSMBAM) neural networks with mixed time delays. The mixed time delays comprise both discrete and distributed time-delays. A series of novel switching functions are proposed to reflect the state-dependent characteristics of the memristive connection weights in the discrete-time setting, which facilitates the dynamics analysis of the addressed memristive neural networks (MNNs). By means of the introduced series of switching functions, an H∞ state estimator is designed such that the estimation error is exponentially mean-square stable and the prescribed H∞ performance requirement is achieved. The gain matrices of the desired estimator are parameterized by utilizing the semi-definite programming method. Finally, a simulation example is employed to demonstrate the usefulness and effectiveness of the proposed theoretical results. More... »

PAGES

771-785

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13042-017-0769-2

DOI

http://dx.doi.org/10.1007/s13042-017-0769-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100106685


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51 schema:description In this paper, the H∞ state estimation problem is investigated for a class of discrete-time stochastic memristive bidirectional associative memory (DSMBAM) neural networks with mixed time delays. The mixed time delays comprise both discrete and distributed time-delays. A series of novel switching functions are proposed to reflect the state-dependent characteristics of the memristive connection weights in the discrete-time setting, which facilitates the dynamics analysis of the addressed memristive neural networks (MNNs). By means of the introduced series of switching functions, an H∞ state estimator is designed such that the estimation error is exponentially mean-square stable and the prescribed H∞ performance requirement is achieved. The gain matrices of the desired estimator are parameterized by utilizing the semi-definite programming method. Finally, a simulation example is employed to demonstrate the usefulness and effectiveness of the proposed theoretical results.
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