DocumentCode
1814036
Title
A comparative study of different methods for realizing DFNN algorithm
Author
Er, Meng Joo ; Wong, Wai Mun ; Wu, Shiqian
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume
3
fYear
1999
fDate
1999
Firstpage
2641
Abstract
Presents a comparative study of different methods for realizing the basic learning algorithm of dynamic fuzzy neural networks (DFNNs). Performances of the least squared estimation, Kalman filter and extended Kalman filter methods used for weight adjustment in the basic learning algorithm of DFNNs in terms of learning speed, neuron requirement, approximation accuracy and noise immunity are evaluated and compared
Keywords
Kalman filters; filtering theory; fuzzy neural nets; learning (artificial intelligence); least squares approximations; nonlinear filters; parameter estimation; approximation accuracy; basic learning algorithm; dynamic fuzzy neural networks; extended Kalman filter; learning speed; least squared estimation; neuron requirement; noise immunity; weight adjustment; Approximation algorithms; Covariance matrix; Erbium; Fuzzy logic; Fuzzy neural networks; Heuristic algorithms; Least squares approximation; Neural networks; Neurons; Performance evaluation;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location
Phoenix, AZ
ISSN
0191-2216
Print_ISBN
0-7803-5250-5
Type
conf
DOI
10.1109/CDC.1999.831327
Filename
831327
Link To Document