Title :
Fuzzy expert systems versus neural networks
Author :
Hayashi, Yoichi ; Buckley, James J. ; Czogala, Ernest
Author_Institution :
Dept. of Comput. & Inf. Sci., Ibaraki Univ., Japan
Abstract :
The authors describe a rule-based fuzzy expert system using a method of approximate reasoning to evaluate the rules when given new data. It is argued that any fuzzy expert system using one block of rules can be approximated. The theory is generalized to networks of neural nets and fuzzy expert systems using multiple interconnected blocks of rules. The authors demonstrate how the neural net is trained, and how the rules in the fuzzy expert system are written. An example illustrating these ideas is presented
Keywords :
expert systems; fuzzy set theory; inference mechanisms; learning (artificial intelligence); uncertainty handling; approximate reasoning; inference mechanisms; learning; multiple interconnected blocks of rules; neural networks; rule-based fuzzy expert system; uncertainty handling; Computer science; Expert systems; Feedforward neural networks; Fuzzy reasoning; Fuzzy sets; Hybrid intelligent systems; Multi-layer neural network; National electric code; Neural networks;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.226902
