Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12910
Title: H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays
Authors: Liu, H
Wang, Z
Shen, B
Alsaadi, FE
Keywords: Discrete time;H∞ state estimation;Memristive neural networks;Stochastic time-delays
Issue Date: 2016
Publisher: Taylor & Francis
Citation: International Journal of General Systems, 45(5): pp. 1 - 15,(2016)
Abstract: This paper deals with the robust (Formula presented.) state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed (Formula presented.) performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov–Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.
URI: http://www.tandfonline.com/doi/full/10.1080/03081079.2015.1106731
http://bura.brunel.ac.uk/handle/2438/12910
DOI: http://dx.doi.org/10.1080/03081079.2015.1106731
ISSN: 0308-1079
Appears in Collections:Dept of Computer Science Research Papers

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