Supporting neural network method for process operation

Patent 5774633 Issued on June 30, 1998. Estimated Expiration Date:

June 30, 2015. 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.

Abstract Claims Description Full Text

Patent References

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Inventors

Assignee

Hitachi, Ltd.

Application

No. 613718 filed on 12/23/1991

US Classes:

706/25, Learning method210/96.1, CONSTITUENT MIXTURE VARIATION RESPONSIVE210/143, AUTOMATIC CONTROL210/614, Controlling process in response to stream constituent or reactant concentration210/709, Controlling process in response to stream condition210/739, Including controlling process in response to a sensed condition706/23, Control706/31, Multilayer feedforward706/903, Control706/906Process plant

Examiners

Primary: Davis, George B.

Attorney, Agent or Firm

Antonelli, Terry, Stout & Kraus

Foreign Patent References

1-275381 JP. 11/13/1989

International Classes

G06F 015/18
G06F 009/44

Foreign Application Priority Data

1989-03-13 JP

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

Williams et al, "A Class of Gradient-Estimating Algorithms for Reinforcement Learning in Neural Networks" IEEE 1st Inter-Conf-on Neural Networks, Jun. 1987
Gherrity et al, "A Learning Algorithm for Analog, Fully Recurrent Neural Networks" Jun. 18, 1980
Rohwer et al, "Training Time-Dependence in Neural Networks" IEEE First Inter. Conf. on Neural Networks, Jun. 1987 Neural Networks Primer AI Expert, Feb. 1989
Wasserman et al, "Neural Networks, Part 2:" 1988 IEEE Expert
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