文献类型：期刊文献

英文题名：Adaptive Zeroing-Type Neural Dynamics for Solving Quadratic Minimization and Applied to Target Tracking

作者：He, Huiting[1]; Jiang, Chengze[2]; Zhang, Yudong[3]; Xiao, Xiuchun[1]; Song, Zhiyuan[1]

机构：[1] College of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China; [2] School of Cyber Science and Engineering, Southeast University, 210096, China; [3] School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, United Kingdom

年份：2021

外文期刊名：arXiv

收录：EI(收录号：20210405532)

语种：英文

外文关键词：Clutter (information theory) - Dynamics - Machine learning

外文摘要：The time-varying quadratic minimization (TVQM) problem, as a hotspot currently, urgently demands a more reliable and faster-solving model. To this end, a novel adaptive coefficient constructs framework is presented and realized to improve the performance of the solution model, leading to the adaptive zeroing-type neural dynamics (AZTND) model. Then the AZTND model is applied to solve the TVQM problem. The adaptive coefficients can adjust the step size of the model online so that the solution model converges faster. At the same time, the integration term develops to enhance the robustness of the model in a perturbed environment. Experiments demonstrate that the proposed model shows faster convergence and more reliable robustness than existing approaches. Finally, the AZTND model is applied in a target tracking scheme, proving the practicality of our proposed model. Copyright ? 2021, The Authors. All rights reserved.