| Title |
Passivity of memristor-based BAM neural networks with different memductance and uncertain delays
|
|---|---|
| Published in |
Cognitive Neurodynamics, April 2016
|
| DOI | 10.1007/s11571-016-9385-1 |
| Pubmed ID | |
| Authors |
R. Anbuvithya, K. Mathiyalagan, R. Sakthivel, P. Prakash |
| Abstract |
This paper addresses the passivity problem for a class of memristor-based bidirectional associate memory (BAM) neural networks with uncertain time-varying delays. In particular, the proposed memristive BAM neural networks is formulated with two different types of memductance functions. By constructing proper Lyapunov-Krasovskii functional and using differential inclusions theory, a new set of sufficient condition is obtained in terms of linear matrix inequalities which guarantee the passivity criteria for the considered neural networks. Finally, two numerical examples are given to illustrate the effectiveness of the proposed theoretical results. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Netherlands | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Lecturer | 2 | 25% |
| Researcher | 1 | 13% |
| Student > Master | 1 | 13% |
| Unknown | 4 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 1 | 13% |
| Economics, Econometrics and Finance | 1 | 13% |
| Neuroscience | 1 | 13% |
| Engineering | 1 | 13% |
| Unknown | 4 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 August 2016.
All research outputs
#19,630,735
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Outputs from Cognitive Neurodynamics
#194
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Outputs of similar age
#211,967
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Outputs of similar age from Cognitive Neurodynamics
#3
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