Abstract

A method and a system 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

• Fujitsu, Neurocomputer and its Application to Robot Control, by S. Nagata, et al., et al.1988-6, vol. 39, No. 3
• 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 hierarchical 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 Water Service Workers' Association)", No. 431, p. 28, Aug. 1970
• Parallel Disributed Processing; "Learning Internal Representations by Error Propagation" Vol. 1: Foundations, The MIT Press 1986 by David E. Rumelhart, James L. McClelland and the PDP Research Group
• 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
• Phoneme Recognition: Neural Networks vs. Hidden Markov Models, IEEE ICASSP Conference by Waibel, et al., 1988
• "Neural Network Model using a normalized inner product as measure of similarity", IEEE ICNN by Macukow, et al., 1988
• "Applications of counterpropagation Networks", Neural Networks, Vol. 1, pp. 131-139 by Robert Hecht-Nielsen, 1988
• "The implementation of Multi-Layer perceptrons on Transputer Networks", PPTBM, Proceedings of the 7th OCCAM User, 1987
• "Neural Networks, Part 2:" IEEE Expert, 1988
• Wasserman etal ,"Neural Networks, Part 2:" 1988 IEEE Expert
• Williams et al,"A Class of Gradient-Estimating Algorithms for Reinforcement Learning in Neural Networks" IEEE Ist Inter. Conf. on Neural Networks, Jun. 1987
• Gherrity et al,"A Learning Algorithm for Analog, Fully Recurrent Neural Networks"Jun. 18, 1998O
• Rohwer et al,"Training Time-Dependence in Neural Networks" IEEE first Inter. Conf. on Neural Networks, Jun. 1987
• "Learning Internal Representations in the Coulomb Energy Network", Scofield, Jul. 1988, pp. 271-27