{"title":"Trade-off analysis between finite-time synchronization and energy consumption for fractional-order two-layer neural networks","authors":"Tian Lan , Baoxian Wang , Jigui Jian , Kai Wu","doi":"10.1016/j.neucom.2025.130872","DOIUrl":null,"url":null,"abstract":"<div><div>This paper investigates the finite-time synchronization (FTS) of fractional-order two-layer neural networks with time delays and explores the energy consumption of their controllers, as well as the trade-off between the synchronization time cost and the controller energy consumption. The study makes the following key contributions: First, a sufficient criterion is derived to guarantee the FTS of fractional-order delayed complex networks by using a lemma based on fractional-order differential inequalities. This approach circumvents a potential methodological flaw present in some prior studies. Additionally, while existing research on FTS has primarily focused on fractional-order complex networks without time delays, this work extends the analysis to systems with time delays, thereby broadening the applicability of the results. Second, a switching controller is proposed to accelerate the synchronization of the error system. Third, a standardized evaluation function is introduced to analyze the trade-off between the FTS time cost and controller energy consumption in fractional-order systems, whereas previous research in this area has mostly focused on integer-order systems. Finally, the validity of the theoretical results is demonstrated through numerical simulations.</div></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":"651 ","pages":"Article 130872"},"PeriodicalIF":6.5000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231225015449","RegionNum":2,"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
This paper investigates the finite-time synchronization (FTS) of fractional-order two-layer neural networks with time delays and explores the energy consumption of their controllers, as well as the trade-off between the synchronization time cost and the controller energy consumption. The study makes the following key contributions: First, a sufficient criterion is derived to guarantee the FTS of fractional-order delayed complex networks by using a lemma based on fractional-order differential inequalities. This approach circumvents a potential methodological flaw present in some prior studies. Additionally, while existing research on FTS has primarily focused on fractional-order complex networks without time delays, this work extends the analysis to systems with time delays, thereby broadening the applicability of the results. Second, a switching controller is proposed to accelerate the synchronization of the error system. Third, a standardized evaluation function is introduced to analyze the trade-off between the FTS time cost and controller energy consumption in fractional-order systems, whereas previous research in this area has mostly focused on integer-order systems. Finally, the validity of the theoretical results is demonstrated through numerical simulations.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.