文献类型：期刊文献

英文题名：Adaptive gradient-aware neural dynamics: Towards fast and accurate solutions for dynamic convex optimization

作者：Jiang, Chengze[1];Ye, Aiping[2];He, Huiting[1];Xiao, Xiuchun[1];Lin, Cong[1,3]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[2]Guangdong Ocean Univ, Sch Math & Comp, Zhanjiang 524088, Peoples R China;[3]Guangdong Ocean Univ, Guangdong Prov Key Lab Intelligent Equipment South, Zhanjiang 524088, Peoples R China

年份：2025

卷号：162

外文期刊名：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

收录：SCI-EXPANDED(收录号：WOS:001601106800004)、、EI(收录号：20254419440043)、Scopus(收录号：2-s2.0-105020390008)、WOS

基金：This work was supported by the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604SSJS00301) ; the Open Fund of Guangdong Provincial Key Laboratory of Intelligent Equipment for South China Sea Marine Ranching (Grant No. 2023B1212030003) ; and the Program for Scientific Research Start-up Funds of Guangdong Ocean University (060302112405) .

语种：英文

外文关键词：Constrained dynamic convex optimization; Adaptive gradient-aware neural dynamics; Zeroing neural dynamics; Gradient neural dynamics; Hopfield networks

外文摘要：Constrained Dynamic Convex Optimization (CDCO) represents a core challenge in many engineering applications, where the objective is to minimize a time-varying cost function subject to dynamically evolving constraints. While recent neural network-based methods have demonstrated potential in addressing CDCO, they still suffer from limitations in convergence and solution accuracy, which restrict their effectiveness in real-world deployments. To overcome these challenges, we propose an Adaptive Gradient-Aware Neural Dynamics (AGAND). As an artificial intelligence model derived from a branch of Hopfield networks, AGAND integrates a gradient-aware term with a time derivative term to enhance solution performance. Our AGAND uses gradient information and time-derivative data to achieve faster and more accurate solutions for CDCO. Besides, the convergence of the model is further boosted by introducing state-aware coefficient with gradient feedback mechanism. Theoretical analysis demonstrates the global convergence of the AGAND, along with a detailed complexity assessment. To further adapt AGAND for practical deployment, a discretization scheme is proposed to facilitate implementation on digital hardware. Comparative experiments with state-of-the-art methods illustrate the competitiveness of our AGAND in terms of convergence and accuracy, achieving Average Steady-state Residual Error (ASSRE) of 3.10 x 10-3 and Convergence Time (CT) of 0.04 s. Finally, a robot kinematics scheme and hyperspectral image target detection are formulated on the basis of our AGAND, demonstrating the feasibility and practical utility of the AGAND in real-world engineering problems.