Traffic modeling and identification using a self-adaptive fuzzy inference network
Traffic modeling and identification is an important aspect of traffic control today. With an increase in the demands on today's transportation network, an efficient system to model and understand the changes in the network is necessary for policy makers to make timely decisions which affect the...
Saved in:
Main Authors: | Tung, Sau Wai, Quek, Chai, Guan, Cuntai |
---|---|
Other Authors: | School of Computer Engineering |
Format: | Conference or Workshop Item |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/97930 http://hdl.handle.net/10220/12408 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
SoHyFIS-Yager : a self-organizing Yager based hybrid neural fuzzy inference system
by: Tung, Sau Wai, et al.
Published: (2013) -
Trading with self-adaptive fuzzy inference system
by: Chew, Yao Kang
Published: (2018) -
Rainfall-runoff modelling using a self-reliant fuzzy inference network with flexible structure
by: Chang, Tak Kwin, et al.
Published: (2020) -
Self-reorganizing TSK fuzzy inference system with BCM theory of meta-plasticity
by: Jacob, Biju Jaseph, et al.
Published: (2013) -
Traffic prediction using a Generic Self-Evolving Takagi-Sugeno-Kang (GSETSK) fuzzy neural network
by: Nguyen, Ngoc Nam, et al.
Published: (2013)