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Proceedings Paper

Application of neural networks to nonlinear filtering
Author(s): William R. Michalson
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Paper Abstract

The application of neural networks to learning nonlinear functions for use in filters and control systems is discussed. Several examples characterize the quality of learning that takes place and the performance of the neural network in the presence of noisy inputs. A final example compares a neural network-based filter to an optimal Kalman filter for a simple quadratic system.

Paper Details

Date Published: 1 November 1992
PDF: 10 pages
Proc. SPIE 1829, Cooperative Intelligent Robotics in Space III, (1 November 1992); doi: 10.1117/12.131700
Show Author Affiliations
William R. Michalson, Worcester Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 1829:
Cooperative Intelligent Robotics in Space III
Jon D. Erickson, Editor(s)

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