U.S. patents available from 1976 to present.
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Supporting method and system for process operation

Patent 5845052 Issued on December 1, 1998. Estimated Expiration Date: Icon_subject January 2, 2016. Estimated Expiration Date is calculated based on simple USPTO term provisions. It does not account for terminal disclaimers, term adjustments, failure to pay maintenance fees, or other factors which might affect the term of a patent.

Patent References

3287649

Self-organizing circuits
Patent #: 4774677
Issued on: 09/27/1988
Inventor: Buckley

Two-dimensional finite impulse response filter arrangements
Patent #: 4805129
Issued on: 02/14/1989
Inventor: David

Neural network training tool
Patent #: 4912647
Issued on: 03/27/1990
Inventor: Wood

Self-organizing circuits
Patent #: 4989256
Issued on: 01/29/1991
Inventor: Buckley

Plant malfunction diagnostic method
Patent #: 5023045
Issued on: 06/11/1991
Inventor: Watanabe, et al.

Distributed parallel processing network wherein the connection weights are generated using stiff differential equations
Patent #: 5046020
Issued on: 09/03/1991
Inventor: Filkin

Waveform analysis apparatus and method using neural network techniques
Patent #: 5092343
Issued on: 03/03/1992
Inventor: Spitzer, et al.

Combustion prediction and discrimination apparatus for an internal combustion engine and control apparatus therefor
Patent #: 5093792
Issued on: 03/03/1992
Inventor: Taki, et al.

Method and a system for selection of time series data
Patent #: 5109475
Issued on: 04/28/1992
Inventor: Kosaka, et al.

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Inventors

Assignee

Application

No. 582018 filed on 01/02/1996

US Classes:

706/33Semiconductor neural network

Examiners

Primary: Davis, George B.

Attorney, Agent or Firm

Foreign Patent References

  • 62-239278 JP. 10/13/1987
  • 1-27538 JP. 11/13/1989

International Classes

G06F 015/18
G06K 009/00

Foreign Application Priority Data

1989-03-13 JP

Abstract

A method for causing a neural circuit model to learn typical past control results of a process and using the neural circuit model for supporting an operation of the process. The neural circuit model is caused to learn by using, as input signals, a typical pattern of values of input variables at different points in time and, as a teacher signal, its corresponding values of the control variable. An unlearned pattern of input variables is inputted to the thus-learned neuron circuit model, whereby a corresponding value of the control variable is determined. Preferably, plural patterns at given time intervals can be simultaneously used as patterns to be learned.

Other References

  • Wasserman et al, "Neural Network, Part 2", IEEE Expert, 1988
  • Widrow et al, "Neural Nets for Adaptive Filtering and Adaptive Pattern Recognition," IEEE 1988
  • Waibel et al, "Phoneme Recognition: Neural Networks vs. Hidden Markov Models," IEEE ICASSP Conf., 1988
  • Macukow et al, "Neural Network Model using a normalized inner product as measure of similarity", IEEE ICNN, 1988
  • Hecht-Nielsen, "Applications of counterpropagation Networks," Neural Networks, vol. 1, pp. 131-139, 1988
  • Beynon et al, "The Implementation of Multi-Layer Perceptrons on Transputer Networks," PPTBM, Proceedings of the 7th OCCAM User, 1987
  • Nagata, et al, "Neurocomputer and its Application to Robot Control", Fujitsu, Jun. 1988, vol. 39, No. 3, pp. 175-184
  • Control Engineering, Feb. 1975, New York, U.S., pp. 50-53, Can A Process "Train" its Control System? by S.J. Bailey
  • Biological Cybernetics, "A herarchical Neural-Network Model for Control and Learning of Voluntary Movement", M. Kawato, et al., vol. 57, No. 3, Oct. 1987
  • Advances in Instrumentation, "Applications of Artificial Neural Systems in Robotic Welding", by B.A. Cleveland, vol. 43, No. PART 04, Jan. 1, 1988
  • "Suidokyokai Zasshi (Journal of the Water Service Workers' Association)", No. 431, p. 28, Aug. 1970
  • Learning Internal Representations in the Coulomb Energy Network, Scofield, Jul. 1988, pp. 271-276
  • "A Class of Gradient-Estimating Algorithms for Reinforcement Learning in Neural Networks" IEEE 1st International Conference on Neural Networks by Ronald J. Williams, Jun. 1987
  • "A Learning Algorithm for Analog Full recurrent Neural Networks" by Michael Gherrity, Jun. 18, 1980
  • "Training Time-Dependence in Neural Networks", IEEE 1st International Conference on Neural Networks by Richard Rohwer, Jun. 1987
  • "Neural Networks Primer" AI Expert, Feb. 1989
  • Layered Neural Nets for Pattern Recognition by Widrow, et al., Speech and Signal Processing, vol. 36, No. 7, Jul. 1988
  • Parallel Distributed Processing, "Learning Internal Representations by Error Propagation", D.E. Rumelhart, vol. 1: Foundations, 1986, The MIT Pres
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