{"title":"Re-Stabilizing Large-Scale Network Systems Using High-Dimension Low-Sample-Size Data Analysis","authors":"Xun Shen;Hampei Sasahara;Jun-ichi Imura;Makito Oku;Kazuyuki Aihara","doi":"10.1109/TETCI.2024.3442824","DOIUrl":null,"url":null,"abstract":"Dynamical Network Marker (DNM) theory offers an efficient approach to identify warning signals at an early stage for impending critical transitions leading to system deterioration in extensive network systems, utilizing High-Dimension Low-Sample-Size (HDLSS) data. It is crucial to explore strategies for enhancing system stability and preventing critical transitions, a process known as re-stabilization. This paper aims to provide a theoretical basis for re-stabilization using HDLSS data by proposing a computational method to approximate pole placement for re-stabilizing large-scale networks. The proposed method analyzes HDLSS data to extract pertinent information about the network system, which is then used to design feedback gain and input placement for approximate pole placement. The novelty of this method lies in adjusting only the diagonal elements of the system matrix, thus simplifying the re-stabilization process and enhancing its practicality. The method is applicable to systems experiencing either saddle-node bifurcation or Hopf bifurcation. A theoretical analysis was performed to examine the perturbation of the maximum eigenvalues of the system matrix using the proposed approximate pole placement method. We validated the proposed method via simulations based on the Holme-Kim model.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1638-1649"},"PeriodicalIF":5.3000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10640356/","RegionNum":3,"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
Dynamical Network Marker (DNM) theory offers an efficient approach to identify warning signals at an early stage for impending critical transitions leading to system deterioration in extensive network systems, utilizing High-Dimension Low-Sample-Size (HDLSS) data. It is crucial to explore strategies for enhancing system stability and preventing critical transitions, a process known as re-stabilization. This paper aims to provide a theoretical basis for re-stabilization using HDLSS data by proposing a computational method to approximate pole placement for re-stabilizing large-scale networks. The proposed method analyzes HDLSS data to extract pertinent information about the network system, which is then used to design feedback gain and input placement for approximate pole placement. The novelty of this method lies in adjusting only the diagonal elements of the system matrix, thus simplifying the re-stabilization process and enhancing its practicality. The method is applicable to systems experiencing either saddle-node bifurcation or Hopf bifurcation. A theoretical analysis was performed to examine the perturbation of the maximum eigenvalues of the system matrix using the proposed approximate pole placement method. We validated the proposed method via simulations based on the Holme-Kim model.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.