Abstract
Artificial neural networks - computational models inspired by the brain - provide several distinctive features relevant to control technology. These include: fine-grained massively parallel architectures; robustness to noise, error and damage; learning and adaptation; and nonlinear analog processing. This paper provides a brief overview of neural networks and discusses several ways they can be used for process identification and control: emulating an existing controller, direct and inverse process modeling, direct adaptive control, optimized nonlinear controller development, and process structure and parameter identification.
Original language | English (US) |
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Title of host publication | 1992 IEEE International Conference on Systems, Man, and Cybernetics |
Subtitle of host publication | Emergent Innovations in Information Transfer Processing and Decision Making, SMC 1992 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 369-374 |
Number of pages | 6 |
ISBN (Electronic) | 0780307208, 9780780307209 |
DOIs | |
State | Published - 1992 |
Event | IEEE International Conference on Systems, Man, and Cybernetics, SMC 1992 - Chicago, United States Duration: Oct 18 1992 → Oct 21 1992 |
Publication series
Name | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
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Volume | 1992-January |
ISSN (Print) | 1062-922X |
Other
Other | IEEE International Conference on Systems, Man, and Cybernetics, SMC 1992 |
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Country/Territory | United States |
City | Chicago |
Period | 10/18/92 → 10/21/92 |
Bibliographical note
Publisher Copyright:© 1992 IEEE.