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» The importance of learning in fuzzy systems
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TNN
2010
174views Management» more  TNN 2010»
13 years 2 months ago
Equivalences between neural-autoregressive time series models and fuzzy systems
Soft computing (SC) emerged as an integrating framework for a number of techniques that could complement one another quite well (artificial neural networks, fuzzy systems, evolutio...
José Luis Aznarte, José Manuel Ben&i...
CEC
2010
IEEE
13 years 8 months ago
An adaptive ensemble of fuzzy ARTMAP neural networks for video-based face classification
A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental le...
Jean-François Connolly, Eric Granger, Rober...
CORR
2008
Springer
216views Education» more  CORR 2008»
13 years 7 months ago
Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem
In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framewor...
Sébastien Destercke, Serge Guillaume, Brigi...
ICPR
2008
IEEE
14 years 8 months ago
Fuzzy rule selection using Iterative Rule Learning for speech data classification
Fuzzy rule base systems have been successfully used for pattern classification. These systems focus on generating a rule-base from numerical input data. The resulting rule-base ca...
Bin Ma, Chng Eng Siong, Haizhou Li, Omid Dehzangi
EUSFLAT
2003
121views Fuzzy Logic» more  EUSFLAT 2003»
13 years 9 months ago
An adaptive learning algorithm for a neo fuzzy neuron
In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic...
Yevgeniy Bodyanskiy, Illya Kokshenev, Vitaliy Kolo...