DocumentCode
2135290
Title
Neufuz: neural network based fuzzy logic design algorithms
Author
Khan, Emdad ; Venkatapuram, Prahlad
Author_Institution
Nat. Semiconductor, Santa Clara, CA, USA
fYear
1993
fDate
1993
Firstpage
647
Abstract
A novel fuzzy logic design, called Neufuz, using neural net learning is proposed. Artificial neural net algorithms are used to generate fuzzy rules and membership functions. The combination of learned fuzzy rules, membership functions, and a fuzzy design technique based on new fuzzy inferencing and defuzzification methods significantly improves performance, accuracy, and reliability and reduces design time. Neufuz also minimizes system cost by optimizing the number of rules and membership functions. Simulation results are very encouraging
Keywords
fuzzy logic; fuzzy set theory; inference mechanisms; logic CAD; neural nets; uncertainty handling; Neufuz; defuzzification; fuzzy inferencing; fuzzy logic design; fuzzy rules; learning; membership functions; neural net; Algorithm design and analysis; Artificial neural networks; Biological neural networks; Embedded system; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Mathematical model; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1993., Second IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0614-7
Type
conf
DOI
10.1109/FUZZY.1993.327412
Filename
327412
Link To Document