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A practical neural network design method for the identification of both the direct transfer function and inverse transfer function of an object plant is proposed. As a practical application of the direct transfer function identifier, a nonlinear plant simulator is also proposed. Simulated and experimental results for a second-order plant show that identification can be satisfactorily achieved and that neural network identifiers can represent nonlinear plant characteristics very well. The characteristics of a neural network direct controller with a feedback control loop, which uses the learning results of the inverse transfer function identifier, is also proposed and confirmed
Published in:
Systems, Man and Cybernetics, IEEE Transactions on
(Volume:23
,
Issue:
1
)
Date of Publication: Jan/Feb 1993
- Page(s):
- 204 - 211
- ISSN :
- 0018-9472
- INSPEC Accession Number:
- 4452406
- Digital Object Identifier :
- 10.1109/21.214778
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Jan/Feb 1993
- Sponsored by :
- IEEE Systems, Man, and Cybernetics Society
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