2022
DOI: 10.1109/access.2021.3139633
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Improved Extended Dissipativity Results for T-S Fuzzy Generalized Neural Networks With Mixed Interval Time-Varying Delays

Abstract: The asymptotic stability and extended dissipativity performance of T-S fuzzy generalized neural networks (GNNs) with mixed interval time-varying delays are investigated in this paper. It is noted that this is the first time that extended dissipativity performance in the T-S fuzzy GNNs has been studied. To obtain the improved results, we construct the Lyapunov-Krasovskii functional (LKF), which consists of single, double, triple, and quadruple integral terms containing full information of the delays and a state… Show more

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Cited by 6 publications
(7 citation statements)
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“…In this paper, we consider interval time-varying delay and interval sampling period instead of constant delay and pointwise sampling period in [20], respectively. Furthermore, some interesting research topics for T-S fuzzy systems can be investigated, e.g., asynchronous non-PDC controller and quantizer [32], [33], dissipativity and dissipative control [15], [16], event-triggered control [33], [34], finitetime control [27]- [32], nonfragile control [14], passivity and VOLUME 10, 2022…”
Section: Discussionmentioning
confidence: 99%
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“…In this paper, we consider interval time-varying delay and interval sampling period instead of constant delay and pointwise sampling period in [20], respectively. Furthermore, some interesting research topics for T-S fuzzy systems can be investigated, e.g., asynchronous non-PDC controller and quantizer [32], [33], dissipativity and dissipative control [15], [16], event-triggered control [33], [34], finitetime control [27]- [32], nonfragile control [14], passivity and VOLUME 10, 2022…”
Section: Discussionmentioning
confidence: 99%
“…Example 3: Consider a nonlinear mass-spring-damper system [31]: M s(t) + g (s(t), ṡ(t)) + f (s(t)) + ϕ 1 (s(t)) w(t) = ϕ 2 (s(t)) u(t), (15) where s(t) is the displacement, M is the mass, u is the input force, w is the disturbance input, g (s, ṡ), f (s), ϕ 1 (s), and ϕ 2 (s) are nonlinear terms with respect to damper, spring, w, and u, respectively.…”
Section: Illustrative Examplesmentioning
confidence: 99%
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“…The numerical studies of the fractional MBD model based on the bone remodeling with the tumor effects (1) have been provided in the current study by using the artificial intelligence (AI) together with the configuration of LVMBPNN [34][35][36][37][38][39][40][41]. Furthermore, the fractional MBD model has been designed to present the analysis of super slow evolvement and superfast progressions by substituting the ordinary integer order derivation in the set of Eq.…”
Section: Fractional Order Mathematical Bone Remodeling Systemmentioning
confidence: 99%
“…The stochastic computing performances through the ANNs based on the local and global operators have been implemented to solve several nonlinear, stiff, complex, and singular models [31][32][33][34][35][36][37][38][39][40][41][42][43]. Recently, these applications have been used to solve the Lane-Emden nonlinear system [44], functional order system [45], singular form of the fractional order equations [46][47][48], periodic differential system [49], delayed differential systems [50] and HIV infection based mathematical models [51,52].…”
Section: Novel Features and Framework Of The Stochastic Solversmentioning
confidence: 99%