Neurocontrol: Concepts and applications

Research output: Contribution to journalConference article

Abstract

Neural networks provide several distinctive features that are of substantial relevance to control technology. These include: accurate approximations of nonlinear functions and nonlinear dynamical systems; compact, efficient implementations; and data-intensive rather than expertise-intensive model and controller development. The benefits of neural networks for control applications are now being realized in numerous domains. We discuss several ways neural networks can be used for modeling and control. For modeling applications, neural networks have been trained to realize "black-box" forward and inverse process models as well as parametric models. Neural network controllers can be developed by emulating existing controllers, by model-free optimization, and by model-based optimization. Examples from deployed applications, available products, and the technical literature illustrate these concepts. We conclude by discussing some important topics for future research: dynamic neural networks, incremental learning, and application-specific network design.

Original languageEnglish (US)
Pages (from-to)2-10
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1962
DOIs
StatePublished - Sep 1 1993
Externally publishedYes
EventAdaptive and Learning Systems II 1993 - Orlando, United States
Duration: Apr 11 1993Apr 16 1993

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