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Proceedings Paper

Parameter estimation for process control with neural networks
Author(s): Tariq Samad; Anoop Mathur
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Paper Abstract

An application of neural networks to the problem of parameter estimation for process systems is described. Neural network parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated using a process simulation, resulting in trained networks that are capable of high generalization. This approach can be used for a variety of parameter estimation applications. A proof-of-concept open-loop delay estimator is described, and extensive simulation results detailed. Some results of other parameter estimation networks are also given. Extensions to recursive and closed-loop identification and application to higher-order processes are discussed.

Paper Details

Date Published: 1 August 1991
PDF: 12 pages
Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); doi: 10.1117/12.45014
Show Author Affiliations
Tariq Samad, Honeywell, Inc. (United States)
Anoop Mathur, Honeywell, Inc. (United States)


Published in SPIE Proceedings Vol. 1469:
Applications of Artificial Neural Networks II
Steven K. Rogers, Editor(s)

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