DocumentCode :
324647
Title :
Topology generation for knowledge-based fuzzy Petri nets
Author :
Ouchi, Yuji ; Tazaki, Eiichiro
Author_Institution :
Dept. of Control & Syst. Eng, Toin Univ., Yokohama, Japan
Volume :
2
fYear :
1998
fDate :
4-9 May 1998
Firstpage :
1286
Abstract :
To implement fuzzy reasoning systems fuzzy Petri nets are very effective for the representation of expert knowledge, and then refined using evolutionary learning techniques. Most of these system, however, lack the ability to refine their network´s topology and thus unable to add new rules to the reformulated rule base. To resolve this kind of problems, we propose a new algorithm to improve the initial domain theory of expert using a heuristic learning based on beam search. The proposed method generates a new node where the node is evaluated in the network. In the results, it can be extended to more general knowledge than the initial one. The proposed method is applied to a medical diagnostic system to verify its validity and effectiveness
Keywords :
Petri nets; fuzzy systems; genetic algorithms; inference mechanisms; knowledge based systems; knowledge representation; learning (artificial intelligence); network topology; search problems; beam search; evolutionary learning; fuzzy Petri nets; fuzzy reasoning; genetic algorithm; heuristic learning; knowledge representation; knowledge-based systems; medical diagnostic system; network topology; rule base; Control systems; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Knowledge engineering; Medical diagnosis; Network topology; Petri nets; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location :
Anchorage, AK
ISSN :
1098-7584
Print_ISBN :
0-7803-4863-X
Type :
conf
DOI :
10.1109/FUZZY.1998.686304
Filename :
686304
Link To Document :
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