Data processing: artificial intelligence – Neural network – Learning task
Patent
1996-05-03
1998-09-15
Voeltz, Emanuel Todd
Data processing: artificial intelligence
Neural network
Learning task
706 25, G06F 1518
Patent
active
058094903
ABSTRACT:
The present invention provides a data selection apparatus which augments a set of training examples with the desired output data. The resulting augmented data set is normalized such that the augmented data values range between -1 and +1 and such that the mean of the augmented data set is zero. The data selection apparatus then groups the augmented and normalized data set into related clusters using a clusterizer. Preferably, the clusterizer is a neural network such as a Kohonen self-organizing map (SOM). The data selection apparatus further applies an extractor to cull a working set of data from the clusterized data set. The present invention thus picks, or filters, a set of data which is more nearly uniformly distributed across the portion of the input space of interest to minimize the maximum absolute error over the entire input space. The output of the data selection apparatus is provided to train the analyzer with important sub-sets of the training data rather than with all available training data. A smaller training data set significantly reduces the complexity of the model building or analyzer construction process.
REFERENCES:
patent: 5111531 (1992-05-01), Grayson et al.
patent: 5142612 (1992-08-01), Skeirik
patent: 5159660 (1992-10-01), Lu et al.
patent: 5263120 (1993-11-01), Bickel
patent: 5276771 (1994-01-01), Manukian et al.
patent: 5335291 (1994-08-01), Kramer et al.
patent: 5353207 (1994-10-01), Keeler et al.
patent: 5386373 (1995-01-01), Keeler et al.
patent: 5428644 (1995-06-01), Kohonen
patent: 5465320 (1995-11-01), Enbutsu et al.
patent: 5477444 (1995-12-01), Bhat et al.
patent: 5479576 (1995-12-01), Watanabe et al.
Geladi, Paul, et al., "Partial Least-Squares Regression: A Tutorial", Analytica Chimica Acta, 185 (1986) pp. 1-17.
Serth, R.W., et al., "Gross Error Detection and Data Reconciliation in Steam-Metering Systems", A.I.Ch.E. Journal, May 1986, vol. 32, No. 5, pp. 733-742.
Serth, R.W., et al., "Detection of Gross Errors in Nonlinearly Constrained Data: A Cast Study", Chem. Eng. Comm. 1987, vol. 51, pp. 89-104.
Moody, J., et al., "Learning with Localized Receptive Fields", Yale University of Dept. of Computer Science, Sep. 1988, pp. 1-11.
Wold, Svante, et al., "Nonlinear PLS Modeling", Chemometrics and Intelligent Laboratory Systems, 7 (1989), pp. 53-65.
Kramer, et al., "Diagnosis Using Backpropagation Neural Networks--Analysis and Criticism", Computers Chem. Engng., vol. 14, No. 2, 1990, pp. 1323-1338.
Helland, Kristian, et al., "Recursive algorithm for partial least squares regression", Chemometrics and Intelligent Laboratory Systems, 14 (1991), pp. 129-137.
Puskorius, G.V., et al., "Decoupled Extended Kalman Filter Training of Feedforward Layered Networks", IEEE, 1991, pp. I-771-I-777.
Kohonen, Teuvo, et al., LVQ.sub.-- PAK: A program package for the correct application of Learning Vector Quantization algorithms, IEEE, 1992, pp. I-725-I-730.
Plutowski, et al., "Selecting concise training sets from clean data", Feb. 1992, pp. 1-45.
Qin, S.J., et al., "Nonlinear PLS Modeling Using Neural Networks", Computers Chem. Engng., vol. 16, No. 4, 1992, pp. 379-391.
Su, Hong-Te, et al., "Integrating Neural Networks with First Principles Models of Dynamic Modeling", IFAC Symp. on DYCOR+, 1992, pp. 77-82.
Kramer, Mark A., et al., "Embedding Theoretical Models in Neural Networks", 1992, ACC/WA14, pp. 475-479.
Kramer, M.A., "Autoassociative Neural Networks", Computers Chem. Engng., vol. 16, No. 4, 1992, pp. 313-328.
deJong, Sijmen, "SIMPLS: an alternative approach to partial least squares regression", Chemometrics and Intelligent Laboratory Systems, 18 (1993), pp. 251-263.
Klimasauskas, C., "Developing a Multiple MACD Market Timing System," Advanced Technology for Developers, vol. 2, Special Issue, 4 Qtr. 1993, pp. 1-47.
Thompson, Michael L., et al., "Modeling Chemical Processes Using Prior Knowledge and Neural Networks", A.I.Ch.E. Journal, Aug. 1994, vol. 40, No. 8, pp. 1328-1340.
Haykin, Simon, "Neural Networks Expand SP's Horizons", IEEE Signal Processing Magazine, Mar. 1996, pp. 24-49.
Mulgrew, Bernard, "Applying Radial Basis Functions", IEEE Signal Processing Magazine, Mar. 1996, pp. 50-65.
Jain, Anik K., et al., Artificial Neural Networks: A Tutorial, Computer, Mar. 1996, vol. 29, No. 3, pp. 31-44.
Shang, Yi and Wah, Benjamin, "Global Optimization for Neural Network Training", Computer, Mar. 1996, vol. 29, No. 3, pp. 45-54.
Serbedzija, Nikola B., "Simulating Artificial Neural Networks on Parallel Architecture", Computer, Mar. 1996, vol. 29, No. 3, pp. 57-63.
Tan, Chew Lim, et al., "An Aritificial Neural Network that Models Human Decision Making", Computer, Mar. 1996, vol. 29, No. 3, pp. 64-70.
Setiono, Rudy, et al., "Symbolic Representation of Neural Networks", Computer, Mar. 1996, vol. 29, No. 3, pp. 71-77.
Zhao, Hong, et al., "Modeling of Activated Sewage Wastewater Treatment Processes Using Integrated Neural Networks and a First Principle Model", IFAC, 13th World Congress, 1996, pp. 1-5.
Westinghouse Electric Corporation, Descriptive Bulletin 21-161, WDPFII System Overview, pp. 1-5.
Westinghouse Electric Corporation, Descriptive Bulletin 21-196, WDPFII WEStation Historian, pp. 1-4.
Westinghouse Electric Corporation, Descriptive Bulletin 21-188, WDPFII Distributed Processing Unit--Series 32, pp. 1-4.
Westinghouse Electric Corporation, Descriptive Bulletin 21-195, WEStation OpCon, pp. 1-4.
Westinghouse Electric Corporation, Descriptive Bulletin DB21-206, Standard Algorithms, pp. 1-8.
Westinghouse Electric Corporation, Descriptive Bulletin 21-189, WDPFII Westnet II Plus Data Highway, pp. 1-2.
Westinghouse Electric Corporation, Descriptive Bulletin 21-177, WDPFII Q-Line Process I/O, pp. 1-2.
White, David A., and Sofge, Donald A. (Ed.), Handbook of Intelligent Control, (pp. 283-356).
Guiver John P.
Klimasauskas Casimir C.
Aspen Technology Inc.
Smithers Matthew
Todd Voeltz Emanuel
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