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Proceedings Paper

Neural-network-based fuzzy logic control system with applications on compliant robot control
Author(s): MawKae Hor; Hui Ling Lu
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Paper Abstract

In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.

Paper Details

Date Published: 10 October 1994
PDF: 9 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188915
Show Author Affiliations
MawKae Hor, Institute of Information Science (Taiwan)
Hui Ling Lu, Institute of Information Science (Taiwan)


Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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