State estimate for fuzzy neural networks with random uncertainties based on sampled-data control
The state estimation problem for a class of T–S fuzzy neural networks (FNNs) is concerned in this paper, where random uncertainties and variable sampling intervals are taken into account. In order to make full use of the characteristic about real sampling pattern, a novel piecewise Lyapunov–Krasovsk...
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| Published in | Journal of the Franklin Institute Vol. 357; no. 1; pp. 635 - 650 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.01.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0016-0032 1879-2693 0016-0032 |
| DOI | 10.1016/j.jfranklin.2019.09.043 |
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| Abstract | The state estimation problem for a class of T–S fuzzy neural networks (FNNs) is concerned in this paper, where random uncertainties and variable sampling intervals are taken into account. In order to make full use of the characteristic about real sampling pattern, a novel piecewise Lyapunov–Krasovskii functional (LKF) in which some free-weighting-matrices are not necessarily positive definite is proposed. The cross terms can be deal with by the Free-Matrix-Based (FMB) inequality technique. By use of an appropriate scheme, new sufficient criteria are derived to guarantee the stability of estimation error system with the maximal allowable upper bound (MAUB) of sampling intervals. Then, the sampled-data controller gain matrix can be synthesized by calculating a set of linear matrix inequalities (LMIs). Compared with the existing results, the obtained criteria are less conservative. Finally, the numerical examples are considered and analyzed by the proposed scheme so as to illustrate the benefit and superiority. |
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| AbstractList | The state estimation problem for a class of T–S fuzzy neural networks (FNNs) is concerned in this paper, where random uncertainties and variable sampling intervals are taken into account. In order to make full use of the characteristic about real sampling pattern, a novel piecewise Lyapunov–Krasovskii functional (LKF) in which some free-weighting-matrices are not necessarily positive definite is proposed. The cross terms can be deal with by the Free-Matrix-Based (FMB) inequality technique. By use of an appropriate scheme, new sufficient criteria are derived to guarantee the stability of estimation error system with the maximal allowable upper bound (MAUB) of sampling intervals. Then, the sampled-data controller gain matrix can be synthesized by calculating a set of linear matrix inequalities (LMIs). Compared with the existing results, the obtained criteria are less conservative. Finally, the numerical examples are considered and analyzed by the proposed scheme so as to illustrate the benefit and superiority. |
| Author | Shi, YanPeng Ge, Chao Park, Ju H. Hua, Changchun |
| Author_xml | – sequence: 1 givenname: Chao surname: Ge fullname: Ge, Chao email: gechao365@126.com organization: Institute of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, PR China – sequence: 2 givenname: YanPeng surname: Shi fullname: Shi, YanPeng organization: Institute of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, PR China – sequence: 3 givenname: Ju H. surname: Park fullname: Park, Ju H. email: jessie@ynu.ac.kr organization: Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 712–749, Republic of Korea – sequence: 4 givenname: Changchun surname: Hua fullname: Hua, Changchun organization: Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, PR China |
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| SubjectTerms | Artificial neural networks Estimating techniques Fuzzy logic Inequality Intervals Linear matrix inequalities Mathematical analysis Matrix Neural networks Numerical analysis Sampling State estimation Uncertainty Upper bounds |
| Title | State estimate for fuzzy neural networks with random uncertainties based on sampled-data control |
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