.png){kind=icon}
返回
Model selection for RBF-ARX models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2022.108723.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Radial basis function network-based autoregressive with exogenous input (RBF-ARX) models are useful in nonlinear system modelling and prediction. The identification of RBF-ARX models includes optimization of the (model lags, number of hidden nodes and state vector) and the parameters of the model. Previous works have usually ignored optimizations of the model's architecture. In this paper, the RBF-ARX architecture, which includes the selection of lags, number of nodes of the RBF network, lag orders and state vector, is encoded into a chromosome and is evolved simultaneously by a genetic algorithm (GA). This combines the advantages of the GA and the variable projection (VP) method to automatically generate a parsimonious RBF-ARX model with a high generalization performance. The highly efficient VP algorithm is used as a local search strategy to accelerate the convergence of the optimization. The experimental results demonstrate the effectiveness of the proposed method. (C) 2022 Elsevier B.V. All rights reserved.
Keyword:
Model selection
RBF-ARX models
Variable projection
Genetic algorithms
Time series prediction
Parameter estimation
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
分享收藏The effects of poloxamer and sodium alginate mixture (Guardix-SG®) on range of motion after axillary lymph node dissection: A single-center, prospective, randomized, double-blind pilot study泊洛沙姆和海藻酸钠混合物的作用 (guardix-sg®) 关于腋窝淋巴结清扫术后的运动范围: 一项单中心，前瞻性，随机，双盲的先导研究.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
查看更多内容