Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology

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

Authors
Yaping Jiang, Guosong Wei, Xueyan Sun, Yueqiang Zhang
Corresponding Author
Yaping Jiang
Available Online March 2016.
DOI
https://doi.org/10.2991/icmmct-16.2016.108How to use a DOI?
Keywords
Noise data, RBF, Neural network, Surrogate model.
Abstract
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.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
Series
Advances in Engineering Research
Publication Date
March 2016
ISBN
978-94-6252-165-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/icmmct-16.2016.108How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yaping Jiang
AU  - Guosong Wei
AU  - Xueyan Sun
AU  - Yueqiang Zhang
PY  - 2016/03
DA  - 2016/03
TI  - Predicting Noisy Data with an Improvement RBF Neural Network for Surrogate Models
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 538
EP  - 541
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.108
DO  - https://doi.org/10.2991/icmmct-16.2016.108
ID  - Jiang2016/03
ER  -