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Proceedings Paper

Connectionist learning systems for control
Author(s): Walter L. Baker; Jay A. Farrell
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Paper Abstract

This paper discusses the application of connectionist learning systems (e. g. artificial neural networks) in the design and implementation of automatic feedback control systems. The benefits of this approach are primarily realized in applica- Uons involving nonlinear dynamical systems. For such problems connectionist learning systems may be used advantageously to: (i) facilitate the control system design and tuning process (ii) improve performance by reducing delays that might otherwise be associated with gain or parameter adaptation and (iii) improve robustness by providing an on-line capability for accommodating some unmodeled dynamics (e. g. nonlinear and time-varying behavior). Several control architectures and connectionist learning systems are described. Preliminry experimental results are also presented. 1.

Paper Details

Date Published: 1 February 1991
PDF: 18 pages
Proc. SPIE 1382, Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods, (1 February 1991); doi: 10.1117/12.25211
Show Author Affiliations
Walter L. Baker, Charles Stark Draper Lab., Inc. (United States)
Jay A. Farrell, Charles Stark Draper Lab., Inc. (United States)


Published in SPIE Proceedings Vol. 1382:
Intelligent Robots and Computer Vision IX: Neural, Biological, and 3D Methods
David P. Casasent, Editor(s)

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