NDLI: Applying Embedded Hybrid ANFIS/Quantum-Tuned BPNN Prediction to Collision Warning System for Motor Vehicle Safety

Content Provider	IEEE Xplore Digital Library
Author	Bao Rong Chang Chung-Ping Young Hsiu Fen Tsai Jian-Jr Lin
Copyright Year	2008
Description	Author affiliation: Dept. of Int. Bus., Sue-Te Univ. (Hsiu Fen Tsai) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Taitung Univ. (Bao Rong Chang; Jian-Jr Lin) \|\| Dept. of Comput. Sci. & Inf. Eng., Nat. Cheng Kung Univ. (Chung-Ping Young)
Abstract	This study is to explore how to realize high-performance collision warning system (CWS), providing the precaution against traffic crash in transit. An embedded hybrid adaptive network-based fuzzy inference system (ANFIS) plus quantum-tuned back-propagation neural network (QT-BPNN) built in the platform with DaVinci+XScale-NAV270 was employed to realize collision warning system and we also installed motor vehicle event data recorder (MVEDR). Finally, experiments and verification of the proposed approach were done successfully to achieve better accuracy and more effectiveness on warning level issuing and event data record to motor vehicle.
Starting Page	3
Ending Page	8
File Size	900568
Page Count	6
File Format	PDF
ISBN	9780769533827
DOI	10.1109/ISDA.2008.271
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-26
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems motor vehicle event data recorder Telecommunication traffic Vehicle crash testing adaptive network-based fuzzy inference system Vehicles Road accidents collision warning system Vehicle safety Neural networks Alarm systems Fuzzy neural networks quantum-tuned back-propagation neural network Fuzzy systems Davinci+XScale-NAV270
Content Type	Text
Resource Type	Article

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