TY - GEN
T1 - SKILL BASED CONTROL BY USING FUZZY NEURAL NETWORK FOR HIERARCHICAL INTELLIGENT CONTROL
AU - Shibata, Takanori
AU - Fukuda, Toshio
AU - Kosuge, Kazuhiro
AU - Arai, Fumihito
AU - Toktta, Masatoshi
AU - Mitsuoka, Toyokazu
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 1992
Y1 - 1992
N2 - In this paper, we present a new architecture of intelligent control system for robotic manipulators. The system is an integrated approach of Neuromorphic and Symbolic control of robotic manipulator, including an applied neural network for the servo control, a knowledge based approximation, and a Fuzzy Neural Network (FNN) for skill based control. The neural network in the servo control level is the numerical manipulation, while the knowledge based part is the symbolic manipulation. In the Neuromorphic control, the neural network compensates for the nonlinearity of the system and the uncertainty in the environment. The knowledge base part develops the control strategy symbolically for the servo level. The FNN is used between the servo control level and the knowledge based part to link numerals to symbols and express human skills through learning. This system is analogous to the human cerebral control structure combined with reflex action. © 1992 IEEE
AB - In this paper, we present a new architecture of intelligent control system for robotic manipulators. The system is an integrated approach of Neuromorphic and Symbolic control of robotic manipulator, including an applied neural network for the servo control, a knowledge based approximation, and a Fuzzy Neural Network (FNN) for skill based control. The neural network in the servo control level is the numerical manipulation, while the knowledge based part is the symbolic manipulation. In the Neuromorphic control, the neural network compensates for the nonlinearity of the system and the uncertainty in the environment. The knowledge base part develops the control strategy symbolically for the servo level. The FNN is used between the servo control level and the knowledge based part to link numerals to symbols and express human skills through learning. This system is analogous to the human cerebral control structure combined with reflex action. © 1992 IEEE
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U2 - 10.1109/IJCNN.1992.226980
DO - 10.1109/IJCNN.1992.226980
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 0780305590
VL - 2
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 81
EP - 86
BT - Proceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PB - IEEE
T2 - 1992 International Joint Conference on Neural Networks, IJCNN 1992
Y2 - 7 June 1992 through 11 June 1992
ER -