Global asymptotic stability of complex-valued neural networks with additive time-varying delays.

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Article in Cognitive neurodynamics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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3 · Its place in the literature

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1 citing paper in PubMed.

Stability and synchronization for complex-valued neural networks with stochastic parameters and mixed time delays.Cognitive neurodynamics · 2023
Article

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2 authors.

K SubramanianDepartment of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram, Tamilnadu 624 302 India.

P MuthukumarDepartment of Mathematics, The Gandhigram Rural Institute - Deemed University, Gandhigram, Tamilnadu 624 302 India.ORCID 0000-0003-1396-9948

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we extensively study the global asymptotic stability problem of complex-valued neural networks with leakage delay and additive time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional and applying newly developed complex valued integral inequalities, sufficient conditions for the global asymptotic stability of proposed neural networks are established in the form of complex-valued linear matrix inequalities. This linear matrix inequalities are efficiently solved by using standard available numerical packages. Finally, three numerical examples are given to demonstrate the effectiveness of the theoretical results.

Indexed as

Additive time-varying delaysComplex-valued neural networksGlobal asymptotic stabilityLeakage delayLyapunov–Krasovskii functional

Identifiers

PMID28559957

DOI10.1007/s11571-017-9429-1

PMCPMC5430246

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