Training a support vector machine with process constraints

Details Training a support vector machine with process constraints Training a support vector machine with process constraints

2006-05-05

2009-10-06

Starks, Jr., Wilbert L (Department: 2129)

Data processing: artificial intelligence

Neural network

C706S045000

Reexamination Certificate

active

07599897

ABSTRACT:
System and method for training a support vector machine (SVM) with process constraints. A model (primal or dual formulation) implemented with an SVM and representing a plant or process with one or more known attributes is provided. One or more process constraints that correspond to the one or more known attributes are specified, and the model trained subject to the one or more process constraints. The model includes one or more inputs and one or more outputs, as well as one or more gains, each a respective partial derivative of an output with respect to a respective input. The process constraints may include any of: one or more gain constraints, each corresponding to a respective gain; one or more Nth order gain constraints; one or more input constraints; and/or one or more output constraints. The trained model may then be used to control or manage the plant or process.

REFERENCES:
patent: 5212765 (1993-05-01), Skeirik
patent: 5826249 (1998-10-01), Skeirik
patent: 6161130 (2000-12-01), Horvitz et al.
patent: 6269323 (2001-07-01), Vapnik et al.
patent: 6327581 (2001-12-01), Platt
patent: 6772057 (2004-08-01), Breed et al.
patent: 6868411 (2005-03-01), Shanahan
patent: 6941301 (2005-09-01), Ferguson et al.
patent: 6944616 (2005-09-01), Ferguson et al.
patent: 7020642 (2006-03-01), Ferguson et al.
patent: 7035467 (2006-04-01), Nicponski
patent: 7062384 (2006-06-01), Rocke et al.
patent: 7123971 (2006-10-01), Piche
patent: 7164798 (2007-01-01), Hua et al.
patent: 7167849 (2007-01-01), Graepel et al.
patent: 7197487 (2007-03-01), Hansen et al.
patent: 2003/0078683 (2003-04-01), Hartman et al.
patent: 2005/0234955 (2005-10-01), Zeng et al.
F. Lauer, G. Bloch, Incorporating Prior Knowledge in Support Vector Machines for Classification: a Review, Hyper Articles En Ligne (HAL), [Online] Mar. 22, 2006, XP007905131 Retrieved from the Internet: URL:http//hal.archives-ouvertes.fr/hal-00021555—v1/> [retrieved on Jul. 9, 2008].
M. Lázaro, F. Pérez-Cruz, A. Artés-Rodríguez, Learning a Function and its Derivative Forcing the Support Vector Expansion, IEEE Signal Processing Letters, [Online] vol. 12, No. 3, Mar. 2005, pp. 194-197, XP002488214 Retrieved from the Internet: URL:10.1109/LSP.2004.840841> [retrieved on Jul. 11, 2008].
B. Sayyar-Rodsari, E. Hartmen, E. Plumer, K. Liano C. Schweiger, Extrapolating Gain-Constrained Neural Networks—Effective Modeling for Nonlinear Control, Proceedings of the 43rdIEEE Conference on Decision and Control (CDC 2004), [Online] vol. 5, Dec. 14, 2004-Dec. 17, 2004, pp. 4964-4971, XP010796407 Retrieved from the Internet: URL:http://dx.doi.org/10.1109/CDC.2004.1429593> [retrieved on Jul. 9, 2008].
V. Kecman, Report 616: Support Vector Machines Basics, Reports of the School of Engineering, University of Auckland, [Online] Apr. 2004, XP002487899 Retrieved from the Internet: URL:http://www.support-vector.ws/Report 616 on SVM, V Kecman.pdf> [retrieved on Jul. 9, 2008].
S. Piché, P. Sabiston, A Disturbance Rejection Based Neural Network Algorithm for Control of Air Pollution Emissions, Proceedings of the 2005 IEEE International Joint Conference on Neural Networks (IJCNN 2005), [Online] vol. 5, Jul. 31, 2005-Aug. 4, 2005, pp. 2937-2941, XP010867064 Retrieved from the Internet: URL:http://dx.doi.org/10.1109/IJCNN.2005.1556392> [retrieved on Jul. 9, 2008].
A. K. Kordon, Hybrid Intelligent Systems for Industrial Data Analysis, Proceedings of the 1stInternational IEEE Symposium on Intelligent Systems (IS 2002), [Online] vol. 1, Sep. 10, 2002-Sep. 12, 2002, pp. 10-15, XP010610783 Retrieved from the Internet: URL:http://dx.doi.org/10.1109/IS 2002.1044221> [retrieved on Jul. 9, 2008].
B. J. De Kruif, T. J. A. De Vries, On Using a Support Vector Machine in Learning Feed-Forward Control, Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2001), [Online] vol. 1, Jul. 8, 2001-Jul. 12, 2001, pp. 272-277, XP010553263 Retrieved from the Internet: URL:http://dx.doi.org/10.1109/AIM.2001.936466> [retrieved on Jul. 9, 2008].
I. Goethals, K. Pelckmans, J. A. K. Suykens, B. De Moor, Identification of MIMO Hammerstein Models Using Least Squares Support Vector Machines, Automatica, [Online] vol. 41, No. 7, Apr. 18, 2005, pp. 1263-1272, XP004891952 Retrieved from the Internet: URL:http://dx.doi.org/10.1016/j.automatica.2005.02.002> [retrieved Jul. 9, 2008].

Affiliated with

Also associated with

Rating

Training a support vector machine with process constraints does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Training a support vector machine with process constraints, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Training a support vector machine with process constraints will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFUS-PAI-O-4066063