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Stability analysis of memristor-based fractional-order neural networks with different memductance functions.
| Content Provider | Europe PMC |
|---|---|
| Author | Rakkiyappan, R. Velmurugan, G. Cao, Jinde |
| 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. |
| Related Links | https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4384520&blobtype=pdf |
| ISSN | 18714080 |
| Volume Number | 9 |
| DOI | 10.1007/s11571-014-9312-2 |
| PubMed Central reference number | PMC4384520 |
| Issue Number | 2 |
| PubMed reference number | 25861402 |
| Journal | Cognitive Neurodynamics [Cogn Neurodyn] |
| e-ISSN | 18714099 |
| Language | English |
| Publisher | Springer Netherlands |
| Publisher Date | 2014-10-09 |
| Publisher Place | Dordrecht |
| Access Restriction | Open |
| Rights License | © Springer Science+Business Media Dordrecht 2014 |
| Subject Keyword | Fractional-order Memristor-based neural networks Banach contraction principle Time delays |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cognitive Neuroscience |
