H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks

DOI

10.2991/ijcis.2011.4.5.11How to use a DOI?

Keywords

H-infinite state estimation, Takagi-Sugeno fuzzy Hopfield neural networks, linear matrix inequality

Abstract

This paper presents a H-infinite state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H performance. The proposed H state estimator can be realized by solving a linear matrix inequality (LMI) problem. An illustrative numerical example is given to verify the effectiveness of the proposed H-infinite state estimator.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Choon Ki Ahn
PY  - 2011
DA  - 2011/09/01
TI  - H-infinite State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 855
EP  - 862
VL  - 4
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.5.11
DO  - 10.2991/ijcis.2011.4.5.11
ID  - KiAhn2011
ER  -