Dissipative filter design for Takagi-Sugeno fuzzy neural networks

This paper proposes a novel dissipative filter for Takagi-Sugeno fuzzy Hopfield neural networks with time varying delay. This filter guarantees (Q, S, R)-a-dissipativity and is regarded as a generalization of some performance indices, such as H ∞ performance, passivity, and mixed H ∞ /passivity. The...

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Published in2015 15th International Conference on Control, Automation and Systems (ICCAS) pp. 181 - 185
Main Authors Kyu-Chul Lee, Hyun-Duk Choi, Dae-Ki Kim, Choon-Ki Ahn, Myo-Taeg Lim
Format Conference Proceeding
LanguageEnglish
Published Institute of Control, Robotics and Systems - ICROS 01.10.2015
Subjects
Dissipative filtering
Linear matrix inequality(LMI)
Neural networks
Takagi-Sugeno fuzzy Hopfield neural network
Takagi-Sugeno model
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Summary:This paper proposes a novel dissipative filter for Takagi-Sugeno fuzzy Hopfield neural networks with time varying delay. This filter guarantees (Q, S, R)-a-dissipativity and is regarded as a generalization of some performance indices, such as H ∞ performance, passivity, and mixed H ∞ /passivity. The linear matrix inequality (LMI) approach solving convex problem is used to obtain a gain matrix satisfying both (Q, S, R)-a-dissipativity and asymptotic stability of the error system. Some simulations are dealt with to validate the performance of the proposed method.
ISSN:2093-7121
DOI:10.1109/ICCAS.2015.7364903