An error passivation approach to filtering for switched neural networks with noise disturbance

Authors:
Choon Ki Ahn
Affiliations:
Seoul National University of Science and Technology, Department of Automotive Engineering, Seoul, Korea
Venue:
Neural Computing and Applications
Year:
2012

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Abstract

In this paper, an error passivation approach is used to derive a new passive and exponential filter for switched Hopfield neural networks with time-delay and noise disturbance. Based on Lyapunov-Krasovskii stability theory, Jensen’s inequality, and linear matrix inequality (LMI), a new sufficient criterion is established such that the filtering error system is exponentially stable and passive from the noise disturbance to the output error. It is shown that the unknown gain matrix of the proposed switched passive filter can be determined by solving a set of LMIs, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed switched passive filter.