NDLI: A fast adaptive neural network system for intelligent control

Content Provider	IEEE Xplore Digital Library
Author	Zaknich, A. Attikiouzel, Y.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Electron. Eng., Western Australia Univ., Nedlands, WA, Australia (Zaknich, A.)
Abstract	An intelligent control system needs to adapt to new dynamics very quickly but also retain knowledge of past dynamics to be able to act effectively and quickly for repeat occurrences. One solution is to model the system with two neural networks in parallel whereby one network is trained a priori with a wide range of historical dynamics while the second one, is allowed to adapt itself to make up the differences between the first model and the real-time dynamics. Within this scheme, as the second network is called to adapt itself, the first one can be progressively trained to learn the new dynamics without adversely affecting the old training. A strategy of this type can be achieved very effectively the modified probabilistic neural network because it is constructed with local radial kernel functions and its adaptation mechanism is computationally simple and very fast. This is demonstrated using a complex nonlinear system whose characteristics suddenly change after initial training and then switch back to the original characteristics. Comparisons are made with other networks to show the important advantages of the modified probabilistic neural network.
Starting Page	1023
Ending Page	1027
File Size	440256
Page Count	5
File Format	PDF
ISBN	0780340531
ISSN	1062922X
DOI	10.1109/ICSMC.1997.638082
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive systems Programmable control Adaptive control Neural networks Intelligent control Switches Dynamic range Real time systems Nonlinear dynamical systems Kernel
Content Type	Text
Resource Type	Article

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