New delay-interval-dependent stability analysis of neutral type BAM neural networks with successive time delay components

Senthilraj, S.; Raja, R.; Jiang, Feng; Zhu, Quanxin; Samidurai, Rajendran

doi:10.1016/j.neucom.2015.07.060

Cited by 20 publications

(14 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%

“…21 Therefore, much attention has been focused on the research of stability of NNNs with distributed and additive TDs. [22][23][24] By utilizing integral inequality together with reciprocally convex combination inequality (RCCI), the asymptotical stability of Hopfield NNNs with distributed and additive TDs was analyzed, 22 and some new stability criteria for NNNs with additive TDs and leakage delay were provided. 23 Through considering more information about the lower and upper bounds of TDs, 24 the robust stability problem for bidirectional associative memory NNNs with additive TDs was discussed.…”

mentioning

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%

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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.

show abstract

- \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%

mentioning

confidence: 99%

See 3 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

New results on stability analysis and extended dissipative conditions for uncertain memristive neural networks with two additive time‐varying delay components and reaction‐diffusion terms

Cao

Xiong

et al. 2020

Intl J Robust & Nonlinear

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This article deals with the problems of exponential stability and extended dissipative analysis for a class of uncertain memristive neural networks (MNNs) with additive time-varying delays and reaction-diffusion terms. On the basis of generalized Lyapunov functional approach, Hardy-Poincarè inequality, Jensen's inequality, as well as some other inequality techniques, it is shown that the issues of exponential stability and extended dissipativity for the uncertain reaction-diffusion MNNs are solvable if a set of linear matrix inequalities (LMIs) proposed are feasible. As a special case, the conditions on exponential stability and extended dissipativity for the uncertain MNNs with additive time-varying delays are also obtained and given in terms of nonlinear matrix inequalities (NMIs). The obtained NMIs can be transformed as general LMIs via using a new quadratic convex combination skill. Moreover, the developed sufficient conditions for the uncertain delayed MNNs with and without reaction-diffusion terms can be easily checked by Matlab LMI control toolbox, and three numerical examples with simulation are provided to show the effectiveness and applicability of the theoretical results.

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New Results on Reachable Sets Bounding for Switched Neural Networks Systems with Discrete, Distributed Delays and Bounded Disturbances

Thuận

Thu

2017

Neural Process Lett

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New delay-interval-dependent stability analysis of neutral type BAM neural networks with successive time delay components

Cited by 20 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

New results on stability analysis and extended dissipative conditions for uncertain memristive neural networks with two additive time‐varying delay components and reaction‐diffusion terms

New Results on Reachable Sets Bounding for Switched Neural Networks Systems with Discrete, Distributed Delays and Bounded Disturbances

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