Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay

Sowmiya, Chandran; Raja, R.; Cao, Jinde; Rajchakit, Grienggrai; Alsaedi, Ahmed

doi:10.1186/s13662-017-1378-9

Cited by 36 publications

(26 citation statements)

References 37 publications

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“…In our other work, a practical continuous‐time estimation problem was solved where a white‐noise–modeled parameter uncertainty exists in the measurement of a radar altimeter. Similar problems that are encountered in the control and estimation of either deterministic or stochastic systems can be found in many engineering fields (see, for example, other works in neural networks that may include Markovian jumps).…”

Section: Introductionmentioning

confidence: 71%

“…, I, I, I, I, I} from the left and byΓ T from the right. Following, again, some matrix manipulations, we find that if (14) is satisfied by R > 0,H,H, then also (11) holds for the same R.…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 77%

“…The proof of the lemma is obtained by substitutingG = R andH = I, > 0 in (14). Following some matrix manipulations and letting → 0, the LMI of (11) is then recovered.…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 99%

“…Following some matrix manipulations and letting → 0, the LMI of (11) is then recovered. To show that (14) leads to (11), we multiply (14) bȳ…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 99%

“…In order to obtain the robust H 2 filter for the nominal system, we multiply (14) from the left and the right by…”

Section: The Robust H 2 Filtermentioning

confidence: 99%

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Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems

Gershon

Shaked

2018

Intl J Robust & Nonlinear

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Summary We consider linear continuous‐time systems with multiplicative noise and polytopic‐type parameter uncertainty, and we address the problems of H∞ and H2 filtering of these systems. These problems are solved by applying a vertex‐dependent Lyapunov function that considerably reduces the overdesign associated with the classical design that is based on a single Lyapunov function for the whole parameter range. A new approach of the Finsler lemma is used that decreases the overdesign entailed in the usual derivation of the robust estimation problem. The developed theory is also extended to the robust gain scheduling case where online measurement is used to improve the estimation. Two examples are given that demonstrate the tractability and applicability of the design methods.

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Section: Introductionmentioning

confidence: 71%

“…, I, I, I, I, I} from the left and byΓ T from the right. Following, again, some matrix manipulations, we find that if (14) is satisfied by R > 0,H,H, then also (11) holds for the same R.…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 77%

“…The proof of the lemma is obtained by substitutingG = R andH = I, > 0 in (14). Following some matrix manipulations and letting → 0, the LMI of (11) is then recovered.…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 99%

“…Following some matrix manipulations and letting → 0, the LMI of (11) is then recovered. To show that (14) leads to (11), we multiply (14) bȳ…”

Section: Lemma 2 (See Our Other Work 17 )mentioning

confidence: 99%

“…In order to obtain the robust H 2 filter for the nominal system, we multiply (14) from the left and the right by…”

Section: The Robust H 2 Filtermentioning

confidence: 99%

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Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems

Gershon

Shaked

2018

Intl J Robust & Nonlinear

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Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals

Manivannan

Samidurai

Cao

et al. 2018

Asian Journal of Control

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The problem of non-fragile extended dissipative control design for a class of generalized neural networks (GNNs) with interval time-delay signals is investigated in this paper. By constructing a suitable Lyapunov-Krasovskii functional (LKF) with double and triple integral terms, and estimating their derivative by using the Wirtinger single integral inequality (WSII) and Wirtinger double integral inequality (WDII) technique respectively, and that is mixed with the reciprocally convex combination (RCC) approach. A new delay-dependent non-fragile extended dissipative control design for GNNs are expressed in terms of the linear matrix inequalities (LMIs). Then, the desired non-fragile extended dissipative controller can be obtained by solving the linear matrix inequalities (LMIs). Furthermore, a non-fragile state feedback controller is designed for GNNs such that the closed-loop system is extended dissiptive. Thus, the non-fragile extended dissipative controller can be adopted to deal with the non-fragile  2 − ∞ performance, non-fragile  ∞ performance, non-fragile passive performance, non-fragile mixed  ∞ and passivity performance, and non-fragile dissipative performance for GNNs by selecting the weighting matrices. Finally, simulation studies are demonstrated for showing the feasibility of the proposed method. Among them, one example was supported by the real-life application of the quadruple tank process system.

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Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

Banu¹,

Banupriya²,

Krishnasamy

et al. 2023

Math Methods in App Sciences

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In this paper, the problem of robust stability analysis for a type of uncertain stochastic switched inertial neural networks (SSINNs) with time-varying delay is investigated. First, the original second-order system is converted into first-order differential equations using the variable transformation method. Next, some sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained using Lyapunov-Krasovskii functional (LKF), state-dependent switching (SDS) method, and Jensen's integral inequality for estimating integral inequalities so that the augmented system is robust, global, and asymptotically stable in the mean square for the uncertain SSINNs with time-varying delay. It is shown that the stability of the above considered system composed of all unstable subsystems can be achieved by using the SDS law. Finally, two numerical simulations are provided to demonstrate the effectiveness of the proposed SDS law.

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Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay

Cited by 36 publications

References 37 publications

Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems

Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems

Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

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Enhanced robust finite-time passivity for Markovian jumping discrete-time BAM neural networks with leakage delay

Cited by 36 publications

References 37 publications

Robust vertex‐dependent H∞ and H2 estimation for stochastic linear systems

Robust vertex‐dependent H∞ and H2 estimation for stochastic linear systems

Non‐Fragile Extended Dissipativity Control Design for Generalized Neural Networks with Interval Time‐Delay Signals

Robust stability of uncertain stochastic switched inertial neural networks with time‐varying delay using state‐dependent switching law

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Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems

Robust vertex‐dependent H_∞ and H₂ estimation for stochastic linear systems