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Proceedings Paper

Ellipsoidal fuzzy learning for smart car platoons
Author(s): Julie A. Dickerson; Bart Kosko
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Paper Abstract

A neural-fuzzy system combined supervised and unsupervised learning to find and tune the fuzzy-rules. An additive fuzzy system approximates a function by covering its graph with fuzzy rules. A fuzzy rule patch can take the form of an ellipsoid in the input-output space. Unsupervised competitive learning found the statistics of data clusters. The covariance matrix of each synaptic quantization vector defined on ellipsoid centered at the centroid of the data cluster. Tightly clustered data gave smaller ellipsoids or more certain rules. Sparse data gave larger ellipsoids or less certain rules. Supervised learning tuned the ellipsoids to improve the approximation. The supervised neural system used gradient descent to find the ellipsoidal fuzzy patches. It locally minimized the mean-squared error of the fuzzy approximation. Hybrid ellipsoidal learning estimated the control surface for a smart car controller.

Paper Details

Date Published: 22 December 1993
PDF: 11 pages
Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); doi: 10.1117/12.165033
Show Author Affiliations
Julie A. Dickerson, Univ. of Southern California (United States)
Bart Kosko, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 2061:
Applications of Fuzzy Logic Technology
Bruno Bosacchi; James C. Bezdek, Editor(s)

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