Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98295
Title: Traffic prediction using a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network
Authors: Nguyen, Ngoc Nam
Quek, Chai
Cheu, Eng Yeow
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Nguyen, N. N., Quek, C., & Cheu, E. Y. (2012). Traffic prediction using a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. The 2012 International Joint Conference on Neural Networks (IJCNN).
Abstract: This paper analyses traffic prediction based on a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network. Traffic prediction is a problem that requires online adaptive systems with high accuracy performance. The proposed GSETSK framework can learn incrementally with high accuracy without any prior assumption about the data sets. To keep an up-to-date fuzzy rule base, a novel `gradual'-forgetting-based rule pruning approach is proposed to unlearn outdated data by deleting obsolete rules. Experiments conducted on real-life traffic data confirm the validity of the design and the accuracy performance of the GSETSK system.
URI: https://hdl.handle.net/10356/98295
http://hdl.handle.net/10220/12367
DOI: http://dx.doi.org/10.1109/IJCNN.2012.6252409
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Google ScholarTM

Check

Altmetric

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.