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» The importance of learning in fuzzy systems
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TNN
2010
174views Management» more  TNN 2010»
14 years 4 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
14 years 10 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»
14 years 9 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
15 years 11 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
55
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EUSFLAT
2003
121views Fuzzy Logic» more  EUSFLAT 2003»
14 years 11 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...