Ellipsoidal fuzzy learning for smart car platoons

Julie A. Dickerson; Bart Kosko

doi:10.1117/12.165033

22 December 1993 Ellipsoidal fuzzy learning for smart car platoons

Julie A. Dickerson, Bart Kosko

Proceedings Volume 2061, Applications of Fuzzy Logic Technology; (1993) https://doi.org/10.1117/12.165033
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

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.

Citation Download Citation

Julie A. Dickerson and Bart Kosko "Ellipsoidal fuzzy learning for smart car platoons", Proc. SPIE 2061, Applications of Fuzzy Logic Technology, (22 December 1993); https://doi.org/10.1117/12.165033

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