{"title":"Static pinning synchronization control of self-triggered coupling dynamical networks","authors":"Lingzhong Zhang , Shengyuan Xu","doi":"10.1016/j.neunet.2024.106798","DOIUrl":null,"url":null,"abstract":"<div><div>In this paper, a new static pinning intermittent control based on resource awareness triggering is proposed. A multi-layer control technique is used to synchronize the coupled neural network. First, a hierarchical network structure including pinned and interaction layers is induced using each pinning strategy. Second, using the ideas of average aperiodic intermittent control (AIC) rate method and constructing an auxiliary function, a new lemma is proposed for the pinning intermittent synchronization of coupled networks, where the upper/lower bound restrictions on each control width for AIC are relaxed. Third, to obtain the desired synchronization behavior, a self-triggering mechanism (STM) is proposed to execute the AIC of the pinned and interaction layers. Moreover, the proposed STM is effective for the actuation of the static pinning impulsive control. The static pinning method modifies the single pinning and switching pinning impulsive control. Finally, the proposed results are applied to Chua’s circuits, oscillators and small-world networks. Experimental results show the performance of the proposed STM which reduces 34.58% the number of control updates compared to a periodically intermittent event-triggered scheme. Further, for large-scale coupled networks, the <span><math><mi>N</mi></math></span>-dimensional Laplacian matrix <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>V</mi></mrow></msub></math></span> can be decomposed into <span><math><mrow><msub><mrow><mi>s</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>×</mo><msub><mrow><mi>s</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></math></span> and <span><math><mrow><mi>N</mi><mo>−</mo><msub><mrow><mi>s</mi></mrow><mrow><mi>p</mi></mrow></msub><mo>×</mo><mi>N</mi><mo>−</mo><msub><mrow><mi>s</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></math></span> dimensions by hierarchical method, thus reducing the complexity of calculation.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"182 ","pages":"Article 106798"},"PeriodicalIF":6.0000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893608024007226","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a new static pinning intermittent control based on resource awareness triggering is proposed. A multi-layer control technique is used to synchronize the coupled neural network. First, a hierarchical network structure including pinned and interaction layers is induced using each pinning strategy. Second, using the ideas of average aperiodic intermittent control (AIC) rate method and constructing an auxiliary function, a new lemma is proposed for the pinning intermittent synchronization of coupled networks, where the upper/lower bound restrictions on each control width for AIC are relaxed. Third, to obtain the desired synchronization behavior, a self-triggering mechanism (STM) is proposed to execute the AIC of the pinned and interaction layers. Moreover, the proposed STM is effective for the actuation of the static pinning impulsive control. The static pinning method modifies the single pinning and switching pinning impulsive control. Finally, the proposed results are applied to Chua’s circuits, oscillators and small-world networks. Experimental results show the performance of the proposed STM which reduces 34.58% the number of control updates compared to a periodically intermittent event-triggered scheme. Further, for large-scale coupled networks, the -dimensional Laplacian matrix can be decomposed into and dimensions by hierarchical method, thus reducing the complexity of calculation.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.