Zhang Zhijun, Sun Xiangliang, Li Xingru, Liu Yiqi
School of Automation Science and Engineering, South China University of Technology, China; Key Library of Autonomous Systems and Network Control, Ministry of Education, China; Jiangxi Thousand Talents Plan, Nanchang University, Nanchang, China; College of Computer Science and Engineering, Jishou University, Jishou, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, China; Shaanxi Provincial Key Laboratory of Industrial Automation, School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, China; School of Information Science and Engineering, Changsha Normal University, Changsha, China; School of Automation Science and Engineering, and also with the Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, China; Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004), China.
School of Automation Science and Engineering, South China University of Technology, China.
Neural Netw. 2025 Apr;184:106968. doi: 10.1016/j.neunet.2024.106968. Epub 2024 Dec 4.
To efficiently solve the time-varying convex quadratic programming (TVCQP) problem under equational constraint, an adaptive variable-parameter dynamic learning network (AVDLN) is proposed and analyzed. Being different from existing varying-parameter and fixed-parameter convergent-differential neural network (VPCDNN and FPCDNN), the proposed AVDLN integrates the error signals into the time-varying parameter term. To do so, the TVCQP problem is transformed into a time-varying matrix equation. Second, an adaptive time-varying design formulation is designed for the error function, and then, the error function is integrated into the time-varying parameter. Furthermore, the AVDLN is designed with the adaptive time-varying design formulation. Moreover, the convergence and robustness theorems of AVDLN are proved by Lyapunov stability analysis, and Mathematical analysis demonstrates that AVDLN possesses a smaller upper bound on the convergence error and a faster error convergence rate than FPCDNN and VPCDNN. Finally, the validity of AVDLN is demonstrated by simulations, and the comparative results prove that the proposed AVDLN has a faster convergence speed and smaller error fluctuation.
为了有效解决等式约束下的时变凸二次规划（TVCQP）问题，提出并分析了一种自适应变参数动态学习网络（AVDLN）。与现有的变参数和固定参数收敛差分神经网络（VPCDNN和FPCDNN）不同，所提出的AVDLN将误差信号整合到了时变参数项中。为此，将TVCQP问题转化为一个时变矩阵方程。其次，为误差函数设计了一种自适应时变设计公式，然后将误差函数整合到了时变参数中。此外，AVDLN是采用自适应时变设计公式设计的。而且，通过李雅普诺夫稳定性分析证明了AVDLN的收敛性和鲁棒性定理，数学分析表明，与FPCDNN和VPCDNN相比，AVDLN在收敛误差上具有更小的上界和更快的误差收敛速度。最后，通过仿真验证了AVDLN的有效性，比较结果证明所提出的AVDLN具有更快的收敛速度和更小的误差波动。
