Parameter estimation for process control with neural networks

Tariq Samad, Anoop Mathur

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Neural networks are applied to the problem of parameter estimation for process systems. Neural network parameter estimators for a given parametrized model structure can be developed by supervised learning. Training examples can be dynamically generated by 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 are 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.

Original languageEnglish (US)
Pages (from-to)149-164
Number of pages16
JournalInternational Journal of Approximate Reasoning
Issue number3-4
StatePublished - 1992


  • delay compensation
  • neural networks
  • neurocontrol
  • parameter estimation
  • process control
  • system identification


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