Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/72656
Title: Fuzzy-neural approach to time series prediction
Authors: Nie, Junhong 
Issue Date: 1994
Source: Nie, Junhong (1994). Fuzzy-neural approach to time series prediction. IEEE International Conference on Neural Networks - Conference Proceedings 5 : 3164-3169. ScholarBank@NUS Repository.
Abstract: This paper presents a fuzzy-neural approach to prediction of nonlinear time series. The underlying mechanism governing the time series, expressed as a set of IF-THEN rules, is discovered by a modified self-organizing counterpropagation network. The task of predicting the future is carried out by a fuzzy predictor on the basis of the extracted rules. We have applied the approach to three well studied time series. Comparative studies with the other approaches on the sunspot, flour prices, and Mackey-Glass chaotic time series suggest that our approach can offer comparable or even better performances. One of the salient features of the approach is that only single leaning epoch is needed, thereby providing a useful paradigm for some situations where the fast learning is critical.
Source Title: IEEE International Conference on Neural Networks - Conference Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/72656
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

26
checked on Jan 19, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.