.png){kind=icon}
返回
Constrained Variable Projection Optimization for Stationary RBF-AR Models
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2020.3034644.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Stationarity is fundamental for time-series modeling and prediction. In this article, we focus on the radial basis function network-based autoregressive (RBF-AR) models which have been widely used in practical applications. Compared to previous work, we give a less-restrictive sufficient condition for the asymptotic stationarity of the RBF-AR model. The parameter estimation of the RBF-AR model is converted to the optimization of a variable projection functional with constraints of stationarity to always derive a stationary model. The constrained evolutionary algorithm is used to solve the optimization problem. Numerical results demonstrate the effectiveness of the proposed method.
Keyword:
Biological system modeling
Optimization
Numerical models
Time series analysis
Radial basis function networks
Predictive models
Data models
Constrained evolutionary algorithm (CEA)
prediction
radial basis function network-based autoregressive (RBF-AR) model
stationarity
variable projection
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
分享收藏
查看更多内容