.png){kind=icon}
返回
New Models for Future Problems Solving by Using ZND Method, Correction Strategy and Extrapolation Formulas
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2924469.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Time-variant problems, which can be classified into future and non-future problems, are often encountered in academia and industry. In a future problem, we only know the information on the current and past time instants, and we have to acquire the next-time-instant solution before the next time instant arrives. Zeroing neural dynamics (ZND) and Zhang et al. discretization (ZeaD) formula group are two essential tools to build discrete-time ZND (DT-ZND) models for future problems solving. The former uses a systematical design formula to build a continuous-time ZND (CT-ZND) model, and the latter is used to transform the CT-ZND model into the discrete-time forms. Many DT-ZND models have been developed to solve various time-variant problems. In light of DT-ZND models and correction strategy, in this paper, we mainly focus on designing and building improved models for future problems solving. Based on the ZND method, extrapolation formulas, and correction steps, new models and corresponding computational algorithms are proposed to solve future optimization and future matrix inversion problems. The numerical experiments are also carried out to demonstrate the superiority of the proposed algorithms.
Keyword:
Future problem
zeroing neural dynamics (ZND)
correction strategy
extrapolation formula
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Robustness Analysis and Robotic Application of Combined Function Activated RNN for Time-Varying Matrix Pseudo Inversion.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Three-step general discrete-time Zhang neural network design and application to time-variant matrix inversion三步通用离散时间Zhang神经网络设计及其在时变矩阵求逆中的应用NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏A New RNN Model With a Modified Nonlinear Activation Function Applied to Complex-Valued Linear EquationsIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容