Euler-Precision ZFD Formula 3NgPFD_G Extended to Future Minimization with Theoretical Guarantees and Numerical Experiments

摘要：

　　In this paper,we propose and investigate a new Eulerprecision 3-node g-proportional finite difference formula also named Zhang finite difference(ZFD)formula 3NgPFD_G,which achieves an error pattern of O(g),where g denotes the sampling gap.Moreover,the proposed formula is extended to future minimization(FM).Specifically,the ZFD formula 3NgPFD_G and its extension to discretize continuous-time Zhang neural network(CTZNN)model for solving FM are first of all put forward.Secondly,the ZFD formula 3NgPFD_G is proved to effectively approximate the 1st-order derivative by Taylor expansion,and results of discrete-time Zhang neural network(DTZNN)model for solving FM are obtained by adopting the formula above to discretize the CTZNN model.Finally,expository numerical approximation and discretization experiments are conducted and analyzed to illustrate the efficacy and superiority of the ZFD formula 3NgPFD_G and its extension to FM.

关键词： Zhang finite difference (ZFD) 1st-order derivative future minimization (FM) theoretical guarantees numerical experiments

作者: Yunong Zhang Huihui Gong Jian Li Huanchang Huang Min Yang

作者单位: School of Information Science and Technology,Sun Yat-sen University,Guangzhou 510006,China;Key Laboratory of Autonomous Systems and Networked Control,Ministry of Education,Guangzhou 510640,China;SYSU-CMU Shunde International Joint Reseach Institute,Shunde 528300,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 171-176

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）