文献类型：期刊文献

英文题名：A Discrete-Time Neurodynamics Scheme for Time-Varying Nonlinear Optimization with Equation Constraints and Application to Acoustic Source Localization

作者：Cui, Yinqiao[1];Song, Zhiyuan[1];Wu, Keer[1];Yan, Jian[1];Chen, Chuncheng[1];Zhu, Daoheng[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2025

卷号：17

期号：6

外文期刊名：SYMMETRY-BASEL

收录：SCI-EXPANDED(收录号：WOS:001517641300001)、、Scopus(收录号：2-s2.0-105009006519)、WOS

基金：This work was supported in part by Natural Science Foundation of China under Grant 62472107; in part by Natural Science Foundation of Guangdong Province, China, under Grant 2023A1515011477, in part by the Demonstration Bases for Joint Training of Postgraduates of Department of Education of Guangdong Province under Grant 202205; in part by the Innovation Team Project of General University in Guangdong Province of China under Grant 2024KCXTD042; in part by the Science and Technology Plan Project of Zhanjiang City under Grant 2022A01063; in part by the Postgraduate Education Innovation Plan Project of Guangdong Ocean University under Grant (202440); in part by the Undergraduate Innovation Team Project of Guangdong Ocean University under Grant CXTD2021019; in part by the Guangdong University Student Science and Technology Innovation Cultivation Special Fund Support Project pdjh2023 a0243; and in part by the Innovation and Entrepreneurship Training Program for College Students of Guangdong Ocean University under Grant S202410566052.

语种：英文

外文关键词：time-varying nonlinear optimization; noise suppression; neurodynamics; acoustic source localization

外文摘要：Nonlinear optimization with equation constraints has wide applications in intelligent control systems, acoustic signal processing, etc. Thus, effectively tackling the nonlinear optimization problems with equation constraints is of great significance for the advancement of these fields. Current discrete-time neurodynamics predominantly addresses unperturbed optimization scenarios, exhibiting inherent sensitivity to external noise, which limits the practical application of these methods. To address this issue, we propose a discrete-time noise-suppressed neurodynamics (DTNSN) model in this paper. First, the model integrates the static optimization stability of the gradient-based neurodynamics (GND) model with the time-varying tracking capability of the zeroing neurodynamics (ZND) model. Then, an integral feedback term is introduced to suppress external noise disturbances, thereby enhancing the robustness of the model. Additionally, to facilitate implementation on digital hardware, we employ an explicit linear three-step discretization method to obtain the proposed DTNSN model. Finally, the convergence performance, noise suppression capability, and practicality of the model are validated through theoretical analysis, numerical simulations, and acoustic source localization experiments. The model is applicable to the fields of intelligent control systems, acoustic signal processing, and industrial automation, providing new tools for real-time optimization in noisy environments.