Nonlinear Dynamics

, Volume 67, Issue 1, pp 573–586

Switched exponential state estimation of neural networks based on passivity theory

Original Paper

Abstract

In this paper, a new exponential state estimation method is proposed for switched Hopfield neural networks based on passivity theory. Through available output measurements, the main purpose is to estimate the neuron states such that the estimation error system is exponentially stable and passive from the control input to the output error. Based on augmented Lyapunov–Krasovskii functional, Jensen’s inequality, and linear matrix inequality (LMI), a new delay-dependent state estimator for switched Hopfield neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. The unknown gain matrix is determined by solving delay-dependent LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.

Keywords

Passivity theory State estimation Switched Hopfield neural networks Linear matrix inequality (LMI) Augmented Lyapunov–Krasovskii functional 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Automotive EngineeringSeoul National University of Science & TechnologySeoulSouth Korea

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