Traffic Prediction using a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) Fuzzy Neural Network

被引：0

作者：

Ngoc Nam Nguyen ^{[1
]}

Quek, Chai ^{[1
]}

Cheu, Eng Yeow ^{[2
]}

机构：

[1] Nanyang Technol Univ, Ctr Computat Intelligence, Singapore, Singapore

[2] ASTAR, Inst Infocomm Res, Singapore, Singapore

来源：

2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2012年

关键词：

SYSTEM; IDENTIFICATION; MODELS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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To keep an up-to-date fuzzy rule base, a novel 'gradual'-forgetting-based rule pruning approach is proposed to unlearn outdated data by deleting obsolete rules.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.

引用

页数：7

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