Title :
A case study of knowledge acquisition: From connectionist learning to an optimized fuzzy knowledge base
Author :
Mohammadian, M. ; Yu, X. ; Smith, J.D.
Author_Institution :
Dept. of Math. & Comput., Central Queensland Univ., Rockhampton, Qld., Australia
Abstract :
An automated knowledge acquisition architecture for docking a truck problem is presented. The architecture consists of a neural network controller a fuzzy rule maker, and a fuzzy controller. The neural network controller is used to learn the driving knowledge from trials. The driving knowledge is then extracted by the fuzzy rule maker to form a driving knowledge rule base. The driving knowledge rule base is further optimized using a genetic algorithm. Computer simulations are presented to show the effectiveness of the architecture
Keywords :
fuzzy control; genetic algorithms; knowledge acquisition; knowledge based systems; learning (artificial intelligence); neurocontrollers; road vehicles; computer simulation; connectionist learning; driving knowledge; driving knowledge rule base; fuzzy controller; fuzzy rule maker; genetic algorithm; knowledge acquisition; neural network controller; optimized fuzzy knowledge base; truck problem; Automatic control; Computer aided software engineering; Computer architecture; Data mining; Fuzzy control; Genetic algorithms; Humans; Knowledge acquisition; Lighting control; Neural networks;
Conference_Titel :
Emerging Technologies and Factory Automation, 1993. Design and Operations of Intelligent Factories. Workshop Proceedings., IEEE 2nd International Workshop on
Conference_Location :
Palm Cove-Cairns, Qld.
Print_ISBN :
0-7803-0985-5
DOI :
10.1109/ETFA.1993.396423
