Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data

Wu, Zheng‐Guang; Shi, Peng; Su, Hongye; Chu, Jian

doi:10.1109/tsmcb.2012.2230441

Cited by 564 publications

(206 citation statements)

References 31 publications

Supporting

Mentioning

206

Contrasting

Order By: Relevance

“…is equal to (27) via pre-and post-multiplying its both sides with matrix I 0 0 G T (29) and its transpose respectively. As for −G(∑…”

Section: Remarkmentioning

confidence: 99%

“…It also has been widely studied in the field of industrial process control, space flight, medical treatment, electric power and economy. During the past few decades, many important results based on kinds of systems have emerged, such as stability analysis [1][2][3][4][5][6], stabilization [7][8][9][10], delay case [11][12][13], output control [14,15], H ∞ control [16][17][18][19] and filtering [20][21][22], robust control [23][24][25], sliding control [26], state estimation [27], fault detection [28], synchronization [29,30], and so on.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller

Liu

Wang

2017

Information

View full text Add to dashboard Cite

Abstract:In this paper, a kind of fault-tolerant controller is proposed to study the stabilization problem of discrete-time Markovian jump systems, whose operation modes are not only partially-available but also unmatched. Here, such general properties of controller are modeled to be a controller having polytopic forms and uncertainties simultaneously. Based on the proposed model, concise conditions for the existence of such a controller are proposed with linear matrix inequality (LMI) forms, which are extended to consider observer design problem too. Compared with the traditional methods, not only is the designed controller more general but also the established results are fault free and could be solved directly. Finally, numerical examples are used to demonstrate the effectiveness of the proposed methods.

show abstract

“…is equal to (27) via pre-and post-multiplying its both sides with matrix I 0 0 G T (29) and its transpose respectively. As for −G(∑…”

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller

Liu

Wang

2017

Information

View full text Add to dashboard Cite

show abstract

“…Lemma 1 (Discrete Jensen Inequality, see [11,13,34]). For any constant positive definite symmetric matrix Z ∈ R n×n , positive integers d 1 and d 2 satisfying d 2 ≥ d 1 , the following inequality holds:…”

Section: Problem Formulationmentioning

confidence: 99%

“…In [11], the passivity conditions were conducted for discrete jump neural networks with mixed time delays, and the case of Markov chain with partially unknown transition probabilities was also considered. More results related to Markovian jump neural networks involving time delays can also be found in [12,13] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

Zhang

Shi

Agarwal

et al. 2016

IEEE Trans. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

Abstract-This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and timevarying delays represented by Takagi-Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are firstly constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov-Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed H∞ and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.

show abstract

“…Because of two kinds of mechanisms contained, it is very suitable to model such actual systems whose structures or parameters change [1,2]. Over the past years, many research topics on MJSs have been extensively studied, like stability analysis [3][4][5][6], stabilization [7][8][9][10][11], robust control [12][13][14][15], adaptive control [16][17][18][19], ∞ filtering and control [20,21], state estimation [22][23][24][25], synchronization [26][27][28][29][30], and so on.…”

Section: Introductionmentioning

confidence: 99%

Observer Design for Delayed Markovian Jump Systems with Output State Saturation

Wang

Feng

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper considers the observer design problem of continuous-time delayed Markovian jump systems with output state saturation. Different from the traditionally observer-based saturation control methods, a kind of system output state saturation with a partially delay-dependent property is proposed, where both nondelay and delay states exist at the same time but happen asynchronously. By exploiting the Bernoulli variable, the probability distributions of such two states are described and considered in the observer design. Based on an improved equality applied to deal with saturation terms, sufficient conditions for the designed observer with three kinds of output saturations are all provided with LMI forms. Finally, a numerical example is given to indicate the effectiveness of the obtained results.

show abstract

Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data

Cited by 564 publications

References 31 publications

Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller

Stabilization of Discrete-Time Markovian Jump Systems by a Partially Mode-Unmatched Fault-Tolerant Controller

Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

Observer Design for Delayed Markovian Jump Systems with Output State Saturation

Contact Info

Product

Resources

About