Robust extended dissipativity analysis for Markovian jump discrete-time delayed stochastic singular neural networks

Nagamani, G.; Soundararajan, G.; Subramaniam, Ramasamy; Azeem, Muhammad

doi:10.1007/s00521-019-04497-y

Cited by 15 publications

(8 citation statements)

References 28 publications

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“…This novel concept includes both a exponential function 𝜇 −l and a constant 𝜌, which is more complex than conventional infinite-time extended dissipativity. 11,19,31,39,41,42 In addition, FTEED can reduce to some important performances by selecting appropriate dissipativity parameters S 1 , S 2 , S 3 , S 4 .…”

Section: Preliminariesmentioning

confidence: 99%

“…11,31,39,40 The extended dissipative filtering problem has been widely investigated in recent years. 5,41,42 It should be emphasized that Reference 11 first designed mode-dependent and mode-independent filters for continuous-time MJSs with time-varying delays such that the resulting filtering error system was stochastically stable and extended dissipative. On the basis of Reference 11, the References 12 and 31 investigated the extended dissipative filtering for discrete time persistent dwell-time switched systems with packet dropouts and continuous time SMJSs with time-varying delays, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, it is worth noticing that extended dissipativity established a unified framework for some well‐known performance indices, such as

{H}_{\infty }

{L}_2-{L}_{\infty }

, passivity, and dissipativity performance by tuning some weighting parameters 11,31,39,40 . The extended dissipative filtering problem has been widely investigated in recent years 5,41,42 . It should be emphasized that Reference 11 first designed mode‐dependent and mode‐independent filters for continuous‐time MJSs with time‐varying delays such that the resulting filtering error system was stochastically stable and extended dissipative.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Finite‐time exponentially extended dissipative filtering for singular time‐delay Markov jump systems with quantized outputs based on hidden Markov mode

Zhao

Zhang

2023

Intl J Robust & Nonlinear

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This article investigates finite‐time exponentially extended dissipative filtering for singular Markov jump systems with time‐varying delays and quantized outputs. The filter and quantizer are both mode‐dependent and their modes are asynchronous with the system modes, which are described by hidden Markov model. A more general performance index named as finite‐time exponentially extended dissipativity is employed to make the concept of extended dissipativity in infinite‐time domain suitable for the finite‐time case. With the aid of a series of novel and effective schemes, sufficient conditions are established to guarantee that the filtering error dynamics are finite‐time stochastically bounded with the exponentially extended dissipative performance. An asynchronous filter is successfully designed by solving a set of linear matrices inequalities. Finally, numerical examples are given to demonstrate the effectiveness of proposed methods.

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Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Besides, it is worth noticing that extended dissipativity established a unified framework for some well‐known performance indices, such as

{H}_{\infty }

{L}_2-{L}_{\infty }

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Finite‐time exponentially extended dissipative filtering for singular time‐delay Markov jump systems with quantized outputs based on hidden Markov mode

Zhao

Zhang

2023

Intl J Robust & Nonlinear

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“…Therefore, it is worthily and necessary to deal with the stability analysis and synchronization design issues of MNNs with partly unknown transition rates. So for, it has been found that MNNs apply to many practical systems and any important conclusions with respect to MNNs, which also have been analyzed in the literature such as stability analysis, control design, reachable set estimation problems [5][6][7], other results in [8,9] and the reference therein.…”

Section: Introductionmentioning

confidence: 99%

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Liu

Shi

et al. 2022

Preprint

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This paper is investigated with the robust finite-time synchronization of discrete-time Markovian jump neural networks with partially unknown transition probabilities. Firstly, a unified framework based on event-triggering approaches and quantization, a synchronization error model is first provided for discrete-time Markovian neural networks. Secondly, with the aid of Lyapunov-Krasovskii functional, sufficient conditions for the finite-time boundedness are deduced which can ensure synchronization of the system model, and the quantized finite-time controller and event-triggered parameters are co-designed so that Markovian neural networks are finite-time bounded by variable decoupled techniques. Finally, a simulation example is established to illustrate the availability of the presented methods.MSC Classification: 15A39 , 24D06 , 93B51 , 93D30 , 93E15

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“…Then, Lyapunov used his second (direct) method that there is no need to solve the differential equations explicitly to investigate the stability of the given systems. The Lyapunov's direct method is still recognized as an effective tool to study the stability theory of dynamical systems such as: the global asymptotic stability of the electrical RLC circuit [8], neural networks with time varying delays [9,10], power systems analysis [11], robot manipulators [12], dissipativity analysis of discretetime neural networks [13], global robust passivity analysis [14], dissipativity and passivity analysis of neural networks [15]. This method is the best way to determine the asymptotic stability or asymptotic controllability of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Novel stability and passivity analysis for three types of nonlinear LRC circuits

Ateş¹,

Kadah²

2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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In this paper, the global asymptotic stability and strict passivity of three types of nonlinear RLC circuits are investigated by utilizing the Lyapunov direct method. The stability conditions are obtained by constructing appropriate Lyapunov function, which demonstrates the practical application of the Lyapunov theory with a clear perspective. The meaning of Lyapunov functions is not clear by many specialists whose studies based on Lyapunov theory. They construct Lyapunov functions by using some properties of Lyapunov functions with much trial and errors or for a system choose candidate Lyapunov functions. So, for a given system Lyapunov function is not unique. But we insist that Lyapunov (energy) function is unique for a given physical system. In this study we highly simplified Lyapunov’s direct method with suitable tools. Our approach constructing energy function based on power-energy relationship that also enable us to take the derivative of integration of energy function. These aspects have not been addressed in the literature. This paper is an attempt towards filling this gap. The results are provided within and are of central importance for the analysis of nonlinear electrical, mechanical, and neural systems which based on the system energy perspective. The simulation results given from Matlab successfully verifies the theoretical predictions.

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Robust extended dissipativity analysis for Markovian jump discrete-time delayed stochastic singular neural networks

Cited by 15 publications

References 28 publications

Finite‐time exponentially extended dissipative filtering for singular time‐delay Markov jump systems with quantized outputs based on hidden Markov mode

Finite‐time exponentially extended dissipative filtering for singular time‐delay Markov jump systems with quantized outputs based on hidden Markov mode

Event-based finite-time quantized synchronization for discrete Markovian neural networks

Novel stability and passivity analysis for three types of nonlinear LRC circuits

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