.png){kind=icon}
返回
Adaptive structure radial basis function network model for processes with operating region migration
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2014.12.030.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
An adaptive structure radial basis function (RBF) network model is proposed in this paper to model nonlinear processes with operating point migration. The recursive orthogonal least squares algorithm (ROLS) is adopted to select new centers on-line, as well as to train the network weights. Based on the R matrix in the orthogonal decomposition, an initial center bank is formed and updated in each sample period. A new learning strategy is proposed to gain information from the new data for network structure adaptation. A center grouping algorithm is also developed to divide the centers into active and non-active groups, so that a structure with a smaller size is maintained in the final network model used for output prediction. The proposed REF model is evaluated and compared with the three existing adaptive structure RBF networks by modeling a nonlinear time-varying numerical example. Simulation results demonstrate that the proposed algorithm has several advantages in term of the adaptive tracking ability and a better recovery speed over the existing methods during the migration of system's operating point. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
REF networks
Neural network model
Adaptive structure network
ROLS algorithm
REF structure adaptation
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:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容