Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control
Copyright © 2021 Elsevier Ltd. All rights reserved..
This paper considers the drive-response synchronization of memristive neural networks (MNNs) with unknown parameters, where the unbounded discrete and bounded distributed time-varying delays are involved. Aiming at the unknown parameters of MNNs, the updating law of weight in response system and the gain of adaptive controller are proposed to realize the synchronization of delayed MNNs. In view of the limited communication and bandwidth, the event-triggered mechanism is introduced to adaptive control, which not only decreases the times of controller update and the amount of data sending out but also enables synchronization when parameters of MNNs are unknown. In addition, a relative threshold strategy, which is relative to fixed threshold strategy, is proposed to increase the inter-execution intervals and to improve the control effect. When the parameters of MNNs are known, the algebraic criteria of synchronization are established via event-triggered state feedback control by exploiting inequality techniques and calculus theorems. Finally, one simulation is presented to validate the effectiveness of the proposed results.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:139 |
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| Enthalten in: |
Neural networks : the official journal of the International Neural Network Society - 139(2021) vom: 15. Juli, Seite 255-264 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Yufeng [VerfasserIn] |
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| Links: |
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| Themen: |
Distributed delays |
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| Anmerkungen: |
Date Completed 02.07.2021 Date Revised 02.07.2021 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.neunet.2021.02.029 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM323868096 |
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| 100 | 1 | |a Zhou, Yufeng |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Synchronization of memristive neural networks with unknown parameters via event-triggered adaptive control |
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| 500 | |a Date Completed 02.07.2021 | ||
| 500 | |a Date Revised 02.07.2021 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2021 Elsevier Ltd. All rights reserved. | ||
| 520 | |a This paper considers the drive-response synchronization of memristive neural networks (MNNs) with unknown parameters, where the unbounded discrete and bounded distributed time-varying delays are involved. Aiming at the unknown parameters of MNNs, the updating law of weight in response system and the gain of adaptive controller are proposed to realize the synchronization of delayed MNNs. In view of the limited communication and bandwidth, the event-triggered mechanism is introduced to adaptive control, which not only decreases the times of controller update and the amount of data sending out but also enables synchronization when parameters of MNNs are unknown. In addition, a relative threshold strategy, which is relative to fixed threshold strategy, is proposed to increase the inter-execution intervals and to improve the control effect. When the parameters of MNNs are known, the algebraic criteria of synchronization are established via event-triggered state feedback control by exploiting inequality techniques and calculus theorems. Finally, one simulation is presented to validate the effectiveness of the proposed results | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Distributed delays | |
| 650 | 4 | |a Event-triggered adaptive control | |
| 650 | 4 | |a Memristive neural networks | |
| 650 | 4 | |a Synchronization | |
| 650 | 4 | |a Unbounded discrete delays | |
| 700 | 1 | |a Zhang, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Zhigang |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neural networks : the official journal of the International Neural Network Society |d 1996 |g 139(2021) vom: 15. Juli, Seite 255-264 |w (DE-627)NLM087746824 |x 1879-2782 |7 nnns |
| 773 | 1 | 8 | |g volume:139 |g year:2021 |g day:15 |g month:07 |g pages:255-264 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1016/j.neunet.2021.02.029 |3 Volltext |
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