Parameter estimation for process control with neural networks

Tariq Samad; Anoop Mathur

doi:10.1117/12.45014

1 August 1991 Parameter estimation for process control with neural networks

Tariq Samad, Anoop Mathur

Proceedings Volume 1469, Applications of Artificial Neural Networks II; (1991) https://doi.org/10.1117/12.45014
Event: Orlando '91, 1991, Orlando, FL, United States

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.

Citation Download Citation

Tariq Samad and Anoop Mathur "Parameter estimation for process control with neural networks", Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); https://doi.org/10.1117/12.45014

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