Predicting Noisy Data with an Improvement RBF Neural Network for Surrogate Models

被引：0
|
作者：
Jiang, Yaping [1 ]
Wei, Guosong [2 ]
Sun, Xueyan [2 ]
Zhang, Yueqiang [1 ]
机构：
[1] China North Vehicle Res Inst, Beijing 100072, Peoples R China
[2] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China
来源：
PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND COMPUTING TECHNOLOGY | 2016年 / 60卷
关键词：
Noise data; RBF; Neural network; Surrogate model;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The process of noise data is a significant issue to the application of data. In this paper, we propose a method of processing noisy data based on improvement radial basis function (RBF) neural network to handle noise data. We establish surrogate models for two kinds of standard functions with noise data and noise-free data respectively, then by means of testing the two models with a set of perfect test data and analyzing the result of the comparative experiments to certify the effectiveness of this method.
引用
收藏
页码：539 / 542
页数：4
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