文献类型：期刊文献

英文题名：A dynamic matrix equation solution method based on NCBC-ZNN and its application on hyperspectral image multi-target detection

作者：He, Huiting[1];Jiang, Chengze[2];Xiao, Xiuchun[1];Wang, Guancheng[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210096, Peoples R China

年份：2023

卷号：53

期号：19

起止页码：22267

外文期刊名：APPLIED INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:001019914800009)、、EI(收录号：20232614303570)、Scopus(收录号：2-s2.0-85162889066)、WOS

基金：AcknowledgementsThis work was supported in part by Natural Science Foundation of Guangdong Province, China under Grants 2023A1515011477, in part by Science and Technology Plan Project of Zhanjiang City under Grant 2022A01063, in part by the Demonstration Bases for Joint Training of Postgraduates of Department of Education of Guangdong Province under Grant 202205, in part by Postgraduate Education Innovation Plan Project of Guangdong Ocean University under Grants (202250, 202251), in part by the Innovation and Entrepreneurship Training Program for College Students of Guangdong Ocean University under Grant 202210566028.

语种：英文

外文关键词：Dynamic matrix equation; Non-convex and bound-constrained; Zeroing neural network (ZNN); Image multi-target detection

外文摘要：For effectively solving dynamic matrix equation (DME) problems, a non-convex and bound-constrained zeroing neural network (NCBC-ZNN) model is designed to relax the convex constraint on the demand of activation function and achieve robustness against various noises. Moreover, rigorous mathematical analyses and proofs are presented under the circumstances of noise-free and noise-perturbed to theoretically investigate the global convergence and robustness of the proposed NCBC-ZNN model. In addition, simulative experiments are further provided to substantiate the superior performance of the proposed model in solving the DME problem compared with the existing state-of-the-art models. Finally, the proposed model combines the constrained energy minimization (CEM) algorithm to solve the hyperspectral image multi-target detection (HIMTD) problem. Compared with existing schemes, our method is competitive in terms of accuracy.