Journal
FUZZY SETS AND SYSTEMS
Volume 394, Issue -, Pages 40-64Publisher
ELSEVIER
DOI: 10.1016/j.fss.2019.09.001
Keywords
T-S fuzzy neural networks; Switched fuzzy sampled-data control; Non-fragile memory filtering; Fuzzy membership function
Funding
- National Natural Science Foundation of China [61703060, 61802036, 61701048, 11601474, 11461082, 61873305]
- Fundamental Research Funds for the Central Universities, Southwest Minzu University [2019NQN07]
- Opening Fund of Geomathematics Key Laboratory of Sichuan Province [scsxdz2018zd02, scsxdz2018zd04]
- Guangxi Natural Science Foundation Project [2017GXNSFBA198179]
- Basic Ability Promotion Project for Young and Middle-aged Teachers in Universities of Guangxi [2018KY0214]
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This paper deals with the non-fragile memory filtering issue of T-S fuzzy delayed neural networks with randomly occurring time-varying parameters uncertainties and variable sampling rates. Compared with existing sampled-data control schemes, an improved switched fuzzy memory sampled-data control protocol is designed for the first time, which involves not only a signal transmission delay but also switched topologies. By developing some new terms and taking full advantage of the variable characteristics related to the actual sampling pattern, a modified loose-looped fuzzy membership functions (FMFs) dependent Lyapunov-Krasovskii functional (LKF) is constructed based on the information of the time derivative of FMFs. Meanwhile, some relaxed matrices chosen in LKF are not consequentially positive definite. Moreover, with the LKF methodology and employing the developed estimation technique, several optimized control algorithms with both a larger sampling period and upper bound of time-varying delays for achieving the stabilization of the resultant T-S fuzzy delayed neural networks are derived. Finally, a numerical example is presented to demonstrate the superiority and applicability of the theoretical results. (C) 2019 Elsevier B.V. All rights reserved.
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