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Proceedings Paper

Synthesis of fuzzy, artificial intelligence and neural networks for hierarchical intelligent control
Author(s): Toshio Fukuda
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Paper Abstract

This chapter presents a new structure of intelligent control for robotic motion. This structure is analogous to the human cerebral control structure for intelligent control. Therefore, the system has a hierarchical structure as an integrated approach of Neuromorphic and Symbolic control, including an applied neural network for servo control, a knowledge based approximation, and a fuzzy set theory for a human interface. The neural network in the servo control level is numerical manipulation, while the knowledge based part is symbolic manipulation. In the neuromorphic control, the neural network compensates for the nonlinearities of the system and uncertainty in its environment. The knowledge base part develops control strategies symbolically for the servo level with a-priori knowledge. The fuzzy logic combined with the neural network is used between the servo control level and the knowledge based part to link numerals to symbols and express human skills through learning.

Paper Details

Date Published: 28 June 1994
PDF: 28 pages
Proc. SPIE 10312, Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing, 1031204 (28 June 1994); doi: 10.1117/12.2283787
Show Author Affiliations
Toshio Fukuda, Nagoya Univ. (Japan)


Published in SPIE Proceedings Vol. 10312:
Neural and Fuzzy Systems: The Emerging Science of Intelligent Computing
Sunanda D. Mitra; Madan M. Gupta; Wolfgang F. Kraske, Editor(s)

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