An activated variable parameter gradient‐based neural network for time‐variant constrained quadratic programming and its applications

作     者：Guancheng Wang Zhihao Hao Haisheng Li Bob Zhang 

作者机构：PAMI Research GroupDepartment of Computer and Information ScienceUniversity of MacaoTaipaMacaoChina China Industrial Control Systems Cyber Emergency Response TeamBeijingChina Beijing Key Laboratory of Big Data Technology for Food SafetyBeijing Technology and Business UniversityBeijingChina 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2023年第8卷第3期

页      面：670-679页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：supported in part by the University of Macao(File No.MYRG2018‐00053‐FST) in part by the Open Research Fund of the Beijing Key Laboratory of Big Data Technology for Food Safety(Project No.BTBD‐2021KF05) in part by the Major Science and Technology Special Project of Yunnan Province(202102AD080006) 

主　　题：computational intelligence mathematics computing optimisation 

摘      要：This study proposes a novel gradient‐based neural network model with an activated variable parameter,named as the activated variable parameter gradient‐based neural network(AVPGNN)model,to solve time‐varying constrained quadratic programming(TVCQP)*** with the existing models,the AVPGNN model has the following advantages:(1)avoids the matrix inverse,which can significantly reduce the computing complexity;(2)introduces the time‐derivative of the time‐varying param-eters in the TVCQP problem by adding an activated variable parameter,enabling the AVPGNN model to achieve a predictive calculation that achieves zero residual error in theory;(3)adopts the activation function to accelerate the convergence *** solve the TVCQP problem with the AVPGNN model,the TVCQP problem is transformed into a non‐linear equation with a non‐linear compensation problem function based on the Karush Kuhn Tucker ***,a variable parameter with an activation function is employed to design the AVPGNN *** accuracy and convergence rate of the AVPGNN model are rigorously analysed in ***,numerical experiments are also executed to demonstrate the effectiveness and superiority of the proposed ***,to explore the feasibility of the AVPGNN model,appli-cations to the motion planning of a robotic manipulator and the portfolio selection of marketed securities are illustrated.