Finite-time convergent gradient-zeroing neurodynamic system for solving temporally-variant linear simultaneous equation

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-02-01 DOI:10.1016/j.asoc.2025.112695

Zhiguo Tan , Yunong Zhang

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引用次数: 0

Abstract

It is known that gradient information plays an essential role in GNS (gradient neurodynamic system) for finding the solution to static problems, and derivative information plays an essential role in ZNS (zeroing neurodynamic system) for finding the solution to temporally-variant problems. This fact prompts us to search for a way to simultaneously utilize them for better performance. Motivated by this point, a novel finite-time convergent GAGZNS (gradient-activation gradient-zeroing neurodynamic system) is designed and proposed to online solve temporally-variant LSE (linear simultaneous equation). The proposed GAGZNS utilizes both gradient information and derivative information, and thus can materialize a faster FTC (finite-time convergence) as compared with the ZNS. The property of FTC and the corresponding upper bound of convergence time are derived through strict theoretical proof and verified through two simulation examples. Finally, on the basis of the AoA (angle-of-arrival) technology, we conduct another example of mobile object localization to exhibit the practicality of the proposed GAGZNS.

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.