Predicting Noisy Data with an Improvement RBF Neural Network for Surrogate Models
Yaping Jiang, Guosong Wei, Xueyan Sun, Yueqiang Zhang
Available Online March 2016.
- https://doi.org/10.2991/icmmct-16.2016.108How to use a DOI?
- Noise data, RBF, Neural network, Surrogate model.
- 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.
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 -