Stochastic sampled-data control for state estimation of time-varying delayed neural networks

Lee, Tae Hee; Park, Ju H.; Kwon, Oh‐Min; Lee, S. M.

doi:10.1016/j.neunet.2013.05.001

Cited by 162 publications

(43 citation statements)

References 35 publications

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“…Therefore, our results have an important significance in theory and in practical applications of NNs with time delays. In addition, the proposed method in this paper can be extendable to many famous dynamical systems, such as cellular NNs [8], state estimation problem [14,15], filtering problem [48], impulsive problem [49,50], Markovian jump NNs [34], and sampled data control problem [47]. This will occur in the near future.…”

Section: Discussionmentioning

confidence: 92%

“…Tank 1 and tank 2 are placed below tank 3 and tank 4 to receive water flow by the action of gravity. As shown in Figure 1, the QTPS can be expressed clearly using the NN model (see, e.g., [31,33,34,46,47]). The differential equation for the mass balances in the QTPS can be expressed as follows: The differential equations describing the mass balances in the delayed equations.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…To develop a more interesting realistic problem, leakage delay can be easily added in QTPS by the inlet of water to the tanks and are meaningful in our practical life. However, until now, this has not been explored in the previous literature (see, e.g., [31,33,34,46,47]). Moreover, in this example, transport delays between valves and tanks being interval timevarying, the following aspects are also taken into account.…”

Section: Numerical Examplesmentioning

confidence: 99%

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Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

et al. 2018

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We examine the stability problem for delayed neutral-type neural networks (NNs) with interval time-varying delay signals under the effects of leakage term by constructing a suitable Lyapunov-Krasovskii functionals (LKFs) with the triple-and four-integral terms and using the famous Jensen inequality, Wirtinger single integral inequality (WSII), and Wirtinger double integral inequality (WDII), combined with the reciprocally convex approach (RCC) for the stability of addressing NNs. Therefore, the major contribution of this study lies in a consideration of new integral inequalities and improved LKFs, fully taking the relationship between the terms in the Leibniz-Newton formula within the framework of linear matrix inequalities (LMIs). Moreover, we assume that the lower bound of interval time-varying delay is not restricted to zero. Using several examples, we show that the proposed stability criterion is less conservative than previous results. Also, the proposed technique is applied to benchmark problem that is associated with reasonable issues to showing feasibility on a real-world problem, including transporting time delay signals and leakage delay as a process variable in the quadruple-tank process system.

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Section: Discussionmentioning

confidence: 92%

Section: Numerical Examplesmentioning

confidence: 99%

Section: Numerical Examplesmentioning

confidence: 99%

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Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

et al. 2018

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“…It is worth noting that, for the filtering error system in (22), the quantization behavior is considered as norm-bounded uncertainty (t), and signal transmission delay and data packet dropout are absorbed into the two successive delays components η m and η(t).…”

Section: Remarkmentioning

confidence: 99%

$$H_{\infty }$$ H ∞ Estimation for Markovian Jump Neural Networks With Quantization, Transmission Delay and Packet Dropout

Zhuang

Xia

et al. 2015

Neural Process Lett

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This paper is concerned with the problem of robust H ∞ estimation for a class of Markovian jump neural networks with norm-bounded parameter uncertainties, time-varying delay and limited communication capacity including signal transmission delay, measurement quantization and data packet dropout, which occur simultaneously in the same networked control system framework. Our objective is to design mode-dependent H ∞ filter in the network environment in order to ensure the filtering error system is not only exponentially mean-square stable, but also satisfies a prescribed H ∞ norm level for the limited communication capacity and for all admissible uncertainties. We use the delay system method to deal with signal transmission delay and data packet dropout, utilize the sector bound approach to handle measurement quantization. We also construct a comprehensive stochastic Lyapunov-Krasovskii functional to show the inherent mode-dependent state delays in the neural networks itself and the network-induced signal transmission delay. The free-weighting matrix technique and a set of strict linear matrix inequalities are employed to derive novel conditions for the desired mode-dependent H ∞ filter. A numerical example is provided to verify the effectiveness and usefulness of the obtained main results.

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“…For example, the state estimation problem for neural networks with a time-varying delay via stochastic sampleddata control was studied in [12]. It is worth mentioning that, in the above mentioned papers, the communication between the neural networks and the estimator is assumed to be perfect.…”

Section: Introductionmentioning

confidence: 99%

H_∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control

Liu¹,

Lee²

2017

The Transactions of The Korean Institute of Electrical Engineer

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-This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with  ∞ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

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Stochastic sampled-data control for state estimation of time-varying delayed neural networks

Cited by 162 publications

References 35 publications

Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

$$H_{\infty }$$ H ∞ Estimation for Markovian Jump Neural Networks With Quantization, Transmission Delay and Packet Dropout

H_∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control

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Stochastic sampled-data control for state estimation of time-varying delayed neural networks

Cited by 162 publications

References 35 publications

Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

Delay-dependent stability criteria for neutral-type neural networks with interval time-varying delay signals under the effects of leakage delay

$$H_{\infty }$$ H ∞ Estimation for Markovian Jump Neural Networks With Quantization, Transmission Delay and Packet Dropout

H∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control

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H_∞ State Estimation of Static Delayed Neural Networks with Non-fragile Sampled-data Control