This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Support Vector Machine Theory Based Shift Quality Assessment for Automated Mechanical Transmission (AMT)
Technical Paper
2007-01-1588
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
In China there is a strong trend in the application of vehicles equipped with automatic transmissions in considering the complexity of traffic and the convenience of automatic transmissions. As a type of automatic transmission, automated mechanical transmission (AMT) shows great potential to be developed as a main transmission because of its simple structures, easy upgrade from manual transmission (MT) and low price.
Support Vector Machine (SVM) is a new statistic method which could make a good prediction with limited training instances. Compared with Artificial Neutral Network (ANN), SVM can provide better genetic ability. In order to verify the ability of the new method, the model trained by one set of AMT car data was applied on some other AMT vehicles, and the predicted results were compared with subjective rating results by expert drivers and analyzed to identify the potential of this new assessment system.
Recommended Content
Technical Paper | Analysis of Tooth Surface Fatigue Strength of Automotive Transmission Gears |
Technical Paper | Basic Study of Shift System for Manual Transmission |
Technical Paper | Measuring Automatic Transmission Shift Performance |
Authors
Topic
Citation
Jian, W., Konghui, G., Yulong, L., and Hua, T., "Support Vector Machine Theory Based Shift Quality Assessment for Automated Mechanical Transmission (AMT)," SAE Technical Paper 2007-01-1588, 2007, https://doi.org/10.4271/2007-01-1588.Also In
References
- Anlin Ge Theory and Design of Vehicle Automatic Drive Beijing China Machine Press 1993
- Yulong Lei Bingzhao Gao Hua Tian Anlin Ge Su Yan “Throttle control strategies in the process of integrated powertrain control” Chinese Journal of Mechanical Engineering (English Edition) 18 3 September 2005 429 433
- Schoeggl Peter Ramschak Erich “Vehicle Driveability Assessment using Neural Networks for Development, Calibration and Quality Tests” SAE 2000-01-0702
- Schöggl P. Koegeler H. M. Gschweitl K. Kokal H. Williams P. Hulak K. “Automated EMS Calibration using Objective Driveability Assessment and Computer Aided Optimization Methods” SAE 2002-01-0849
- Wheals J.C. Crewe C. Ramsbottom M. Rook S. Westby M. “Automated Manual Transmissions - A European Survey and Proposed Quality Shift Metrics” SAE 2002-01-0929
- Vapni k V. Statistical Learning Theory Wiley New York, NY 1998
- Vapnik V. The Nature of Statistical Learning Theory 2nd Springer 2000
- Christianini Nello Shawe-Taylor John An Introduction to Support Vector Machines and Other Kernel-based Learning Methods Beijing China Machine Press 2005
- Huang Quanan Wang Huiyi “Fundamental Study of Jerk: Evaluation of Shift Quality and Ride Comfort” SAE 2004-01-2065
- Kolmanovsky V. Gilbert E. G. “Support Vector Machine-Based Determination of Gasoline Direct Injected Engine Admissible Operating Envelope” SAE 2002-01-1301
- Chi-Man Vong Pak-Kin Wong Yi-Ping Li “Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference” Engineering Applications of Artificial Intelligence 19 3 April 2006 277 287
- Hsu Chih-Wei Chang Chih-Chung Lin Chih-Jen “A Patical Guide to Support Vector Classification” http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf
- Chang Chih-Chung Lin Chih-Jen LIBSVM: a library for support vector machines 2001 http://www.csie.ntu.edu.tw/∼cjlin/libsvm