Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay

Samidurai, Rajendran; Rajavel, S.; Sriraman, Ramalingam; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E.

doi:10.1007/s12555-016-9483-1

Cited by 39 publications

(22 citation statements)

References 41 publications

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“…In detail, the interval [−δ,0] is decomposed into some time‐delay subintervals, such as [−δ i ( t ),0], [−δ( t ),−δ i ( t )], [−δ i ( t )−δ j ,−δ( t )] and [−δ,−δ i ( t )−δ j ] for i , j =1,2, i ≠ j . It is clear that the above subintervals reflect the evolution process of additive TDs more precisely, which is more realistic than References . The subintervals are then employed to estimate the bound of

- \int_{t - δ}^{t} ż^{T} (α) R_{1} ż (α) d α

and

- \int_{t - δ}^{t} ż^{T} (α) R_{2} ż (α) d α

in different ways, respectively, so that the information about additive TDs is taken into account more sufficiently.…”

Section: Resultsmentioning

confidence: 99%

“…As a result, some conservativeness could be generated if different kinds of TDs are regarded as the same . Therefore, much attention has been focused on the research of stability of NNNs with distributed and additive TDs . By utilizing integral inequality together with reciprocally convex combination inequality (RCCI), the asymptotical stability of Hopfield NNNs with distributed and additive TDs was analyzed, and some new stability criteria for NNNs with additive TDs and leakage delay were provided .…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, previous works only investigated the FB problems for NNNs with distributed TD but did not consider the additive TDs and SMJU parameters. Furthermore, the SMJU parameters are not considered in References , and the distributed and additive TDs are not taken into account in References . Moreover, it has been shown that the RCCI is very helpful to reduce the conservative of stability conditions .…”

Section: Introductionmentioning

confidence: 99%

“…Note that most of the previous works [14][15][16][17][18][19][20][21][22][23][24] mainly discussed the stability of NNs with mixed TDs over an infinite time. However, high values of the states are not permitted in many practical applications, and it always needs to concern the transient behaviors and performances of systems in a finite-time interval.…”

mentioning

confidence: 99%

“…= col{e 3 − e 5 , −e 3 − e 5 + 2e16 }, 14 = col{e 5 − e 10 , −e 5 − e 10 + 2e 17 },21 = col{e 1 − e 4 , −e 1 − e 4 + 2e 18 },22 = col{e 8 − e11 , −e 8 − e 11 + 2e 19 },23 = col{e 4 − e 5 , −e 4 − e 5 + 2e 20 }, 24 = col{e 5 − e 8 , −e 5 − e 8 + 2e21 }, 31 = col{e 22 , e 22 − 2e 23 }, 32 = col{e 24 , e 24 − 2e 25 }, 41 = col{e 26 , e 26 − 2e 27 }, 42 = col{e 28 , e 28 − 2e 29 },…”

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Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Zhang

Qiu

Liu

et al. 2019

Intl J Robust & Nonlinear

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Summary This article investigates the stochastic robust finite‐time boundedness problem for semi‐Markov jump uncertain (SMJU) neutral‐type neural networks with distributed and additive time‐varying delays (TDs). To derive less conservative stability criteria, a generalized reciprocally convex combination inequality (RCCI) is first proposed, which includes the existing RCCIs as its special cases. By taking full advantage of the characteristics of various TDs and SMJU parameters, a novel suitable Lyapunov‐Krasovskii functional is provided. Then, with the virtue of the new RCCI and other analysis approaches, some new criteria guaranteeing the underlying systems are stochastically robustly finite‐time bounded or stable and are derived in the form of linear matrix inequalities. Finally, three numerical examples are given to show the validity of the approaches presented in this article.

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- \int_{t - δ}^{t} ż^{T} (α) R_{1} ż (α) d α

and

- \int_{t - δ}^{t} ż^{T} (α) R_{2} ż (α) d α

in different ways, respectively, so that the information about additive TDs is taken into account more sufficiently.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Zhang

Qiu

Liu

et al. 2019

Intl J Robust & Nonlinear

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Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Saravanakumar

Nirmala²,

Raja

et al. 2019

Asian Journal of Control

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This work reports the result of finite-time reliable dissipative control for switched neutral-type based neural networks subject to input nonlinearities, time-delay and randomly occurring perturbations. In particular, a novel control model consist of both linear and non-linear fault inputs is designed for reliable control. In addition, randomly occurring uncertainty caused by stochastic variables satisfying the Bernoulli distribution is considered. Based on Lyapunov stability theory, Jensen's integral inequality technique and average dwell time method, sufficient criteria for finite-time boundedness of resulting neural networks are obtained. Furthermore, the obtained boundedness theory are enhanced to solve the finite-time dissipative problem of the considered systems through reliable control against mixed actuator failures. The obtained sufficient conditions are constructed in the form of linear matrix inequalities, which can be facilitated by using some standard numerical Matlab packages. At last, numerical simulations are presented to demonstrate the effectiveness of the considered theory. KEYWORDS finite-time boundedness, finite-time dissipative, neutral-type of switched neural networks, reliable control, time-delay How to cite this article: Saravanakumar T, Nirmala VJ, Raja R, Cao J, Lu G. Finite-time reliable dissipative control of neutral-type switched artificial neural networks with nonlinear fault inputs and randomly occurring uncertainties.

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Almost sure stability of stochastic neutral Cohen–Grossberg neural networks with Lévy noise and time‐varying delays

Deng

2022

Asian Journal of Control

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The almost sure stability for the stochastic neutral Cohen–Grossberg neural networks (SNCGNNs) with Lévy noise, time‐varying delays, and Markovian switching would be deliberated in this article. By means of the nonnegative semimartingale convergence theorem (NSCT), the neutral Itô formula, M‐matrix method, and selecting appropriate Lyapunov function, several almost sure stability criterions for the SNCGNNs could be derived. Moreover, according to the M‐matrix theory, the upper bounds of the coefficients at any mode are given. Finally, two examples and numerical simulations verify the correctness of theoretical analysis for the stability criterions proposed in the article.

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Novel results on stability analysis of neutral-type neural networks with additive time-varying delay components and leakage delay

Cited by 39 publications

References 41 publications

Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Stochastic robust finite‐time boundedness for semi‐Markov jump uncertain neutral‐type neural networks with mixed time‐varying delays via a generalized reciprocally convex combination inequality

Finite‐time reliable dissipative control of neutral‐type switched artificial neural networks with non‐linear fault inputs and randomly occurring uncertainties

Almost sure stability of stochastic neutral Cohen–Grossberg neural networks with Lévy noise and time‐varying delays

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