BY USING FUZZY, ARTIFICIAL INTELLIGENCE, AND NEURAL NETWORK
Toshio Fukuda, Takanori Shibata · 1992
We present a new structure of intelligent conuol for robotic motion. This syst the human cerebral control structure for intelligent con-trol. 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 nonlinearity 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.