DocumentCode
285101
Title
On the equivalence of neural networks and fuzzy expert systems
Author
Buckley, James J. ; Hayashi, Yoichi ; Czogala, Ernest
Author_Institution
Dept. of Maths., Alabama Univ., Birmingham, AL, USA
Volume
2
fYear
1992
fDate
7-11 Jun 1992
Firstpage
691
Abstract
It is proven that any continuous, layered, feedforward neural net can be approximated to any degree of accuracy by a (discrete) fuzzy expert system, and that any continuous, discrete, fuzzy expert system with one block of rules may be approximated to any degree of accuracy by a three layered, feedforward neural net. The second result may be generalized to multiple blocks of rules by considering total (discrete) input and total (discrete) output from the fuzzy expert system. It is concluded that fuzzy expert systems and neural nets can both approximate functions (mappings, systems)
Keywords
expert systems; feedforward neural nets; fuzzy logic; discrete fuzzy expert systems; equivalence; feedforward neural net; fuzzy expert systems; neural networks; Computer science; Equations; Feedforward neural networks; Fuzzy sets; Hybrid intelligent systems; Logistics; Mathematics; Neural networks; Neurons; Virtual manufacturing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location
Baltimore, MD
Print_ISBN
0-7803-0559-0
Type
conf
DOI
10.1109/IJCNN.1992.226907
Filename
226907
Link To Document