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Type: Journal article
Title: Robust estimation for neural networks with randomly occurring distributed delays and Markovian jump coupling
Author: Xu, Y.
Lu, R.
Shi, P.
Tao, J.
Xie, S.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(4):845-855
Publisher: IEEE
Issue Date: 2018
ISSN: 2162-237X
Statement of
Yong Xu, Renquan Lu, Peng Shi, Jie Tao, and Shengli Xie
Abstract: This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the ι₂-ι∞ performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.
Keywords: Distributed delays; Markovian jump coupling; neural networks; parameter uncertainty; robust state estimator
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TNNLS.2016.2636325
Grant ID: 61573112
Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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