Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks
© 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques...
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th-cmuir.6653943832-704122020-10-14T08:46:07Z Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks G. Rajchakit P. Chanthorn M. Niezabitowski R. Raja D. Baleanu A. Pratap Computer Science Neuroscience © 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results. 2020-10-14T08:30:02Z 2020-10-14T08:30:02Z 2020-12-05 Journal 18728286 09252312 2-s2.0-85090051072 10.1016/j.neucom.2020.07.036 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090051072&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70412 |
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Asia |
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Thailand Thailand |
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Computer Science Neuroscience |
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Computer Science Neuroscience G. Rajchakit P. Chanthorn M. Niezabitowski R. Raja D. Baleanu A. Pratap Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| description |
© 2020 Elsevier B.V. This paper analyzes the stability and passivity problems for a class of memristor-based fractional-order competitive neural networks (MBFOCNNs) by using Caputo's fractional derivation. Firstly, impulsive effects are taken well into account and effective analysis techniques are used to reflect the system's practically dynamic behavior. Secondly, by using the Lyapunov technique, some sufficient conditions are obtained by linear matrix inequalities (LMIs) to ensure the stability and passivity of the MBFOCNNs, which can be effectively solved by the LMI computational tool in MATLAB. Finally, two numerical models and their simulation results are given to illustrate the effectiveness of the proposed results. |
| format |
Journal |
| author |
G. Rajchakit P. Chanthorn M. Niezabitowski R. Raja D. Baleanu A. Pratap |
| author_facet |
G. Rajchakit P. Chanthorn M. Niezabitowski R. Raja D. Baleanu A. Pratap |
| author_sort |
G. Rajchakit |
| title |
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| title_short |
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| title_full |
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| title_fullStr |
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| title_full_unstemmed |
Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| title_sort |
impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks |
| publishDate |
2020 |
| url |
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85090051072&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70412 |
| _version_ |
1681752897984921600 |