Norm-based zeroing neural dynamics for time-variant non-linear equations  

作　　者：Linyan Dai Hanyi Xu Yinyan Zhang Bolin Liao 

机构地区：[1]College of Cyber Security,Jinan University,Guangzhou,China [2]Pazhou Lab,Guangzhou,China [3]College of Information Science and Engineering,Jishou University,Jishou,China

出　　处：《CAAI Transactions on Intelligence Technology》2024年第6期1561-1571,共11页智能技术学报(英文)

基　　金：Natural Science Foundation of China,Grant/Award Number:62206109;Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2022A1515010976;Young Scholar Program of Pazhou Lab,Grant/Award Number:PZL2021KF0022;National College Student Innovation and Entrepreneurship Training Program,Grant/Award Number:202410559070。

摘　　要：Zeroing neural dynamic(ZND)model is widely deployed for time-variant non-linear equations(TVNE).Various element-wise non-linear activation functions and integration operations are investigated to enhance the convergence performance and robustness in most proposed ZND models for solving TVNE,leading to a huge cost of hardware implementation and model complexity.To overcome these problems,the authors develop a new norm-based ZND(NBZND)model with strong robustness for solving TVNE,not applying element-wise non-linear activated functions but introducing a two-norm operation to achieve finite-time convergence.Moreover,the authors develop a discretetime NBZND model for the potential deployment of the model on digital computers.Rigorous theoretical analysis for the NBZND is provided.Simulation results substantiate the advantages of the NBZND model for solving TVNE.

关 键 词：finite-time convergence norm-based zeroing neural dynamics ROBUSTNESS time-variant nonlinear equation zeroing neural dynamic 

分 类 号：O17[理学—数学]