Evidence map›Paper›PMID 25861402›Full record

ArticleCognitive neurodynamics2015

Stability analysis of memristor-based fractional-order neural networks with different memductance functions.

R Rakkiyappan, G Velmurugan, Jinde Cao

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Article in Cognitive neurodynamics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
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5citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

R RakkiyappanDepartment of Mathematics, Bharathiar University, Coimbatore, 641 046 Tamilnadu India.
G VelmuruganDepartment of Mathematics, Bharathiar University, Coimbatore, 641 046 Tamilnadu India.
Jinde CaoDepartment of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 Jiangsu China ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, the problem of the existence, uniqueness and uniform stability of memristor-based fractional-order neural networks (MFNNs) with two different types of memductance functions is extensively investigated. Moreover, we formulate the complex-valued memristor-based fractional-order neural networks (CVMFNNs) with two different types of memductance functions and analyze the existence, uniqueness and uniform stability of such networks. By using Banach contraction principle and analysis technique, some sufficient conditions are obtained to ensure the existence, uniqueness and uniform stability of the considered MFNNs and CVMFNNs with two different types of memductance functions. The analysis results establish from the theory of fractional-order differential equations with discontinuous right-hand sides. Finally, four numerical examples are presented to show the effectiveness of our theoretical results.

Indexed as

Banach contraction principleFractional-orderMemristor-based neural networksTime delays

Identifiers

PMID25861402
PMCPMC4384520

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.