Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction
| 详细信息 | 推荐本文 | |
- 作者：Rahib H. Abiyev (1) rahib@neu.edu.tr
- 关键词：Time series prediction – ; Fuzzy wavelet neural network – ; Prediction of exchange rates – ; Neuro ; fuzzy modeling
- 刊名：Neural Computing & Applications
- 出版时间：March 2011
- 出版年：2011
- 期刊代码：113_0941-0643
- 类别：cp
- 卷：20
- 期：2
- 页码：249-259
- 数据来源：sp
摘要
This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.