State estimation for T-S fuzzy Hopfield neural networks via strict output passivation of the error system
- Authors
- Ahn, Choon Ki
- Issue Date
- 1-7월-2013
- Publisher
- TAYLOR & FRANCIS LTD
- Keywords
- strict output passivation; state estimation; TakagiSugeno fuzzy Hopfield neural networks; linear matrix inequality (LMI); LyapunovKrasovskii functional
- Citation
- INTERNATIONAL JOURNAL OF GENERAL SYSTEMS, v.42, no.5, pp.503 - 518
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF GENERAL SYSTEMS
- Volume
- 42
- Number
- 5
- Start Page
- 503
- End Page
- 518
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/102741
- DOI
- 10.1080/03081079.2013.780052
- ISSN
- 0308-1079
- Abstract
- This article presents a new design scheme for the state estimator for TakagiSugeno fuzzy delayed Hopfield neural networks that uses strict output passivation of the error system. Based on LyapunovKrasovskii functional, Jensens inequality, and linear matrix inequality (LMI) formulation, a new delay-dependent criterion is proposed such that makes the resulting estimation error system exponentially stable and passive from the input vector to the output error vector. The unknown gain matrix of the proposed state estimator can be obtained by solving the LMI, which can be facilitated using existing numerical packages. We verify the effectiveness of the proposed state estimation method through a numerical example.
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Collections - College of Engineering > School of Electrical Engineering > 1. Journal Articles
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