Parameter selection algorithm with self adaptive growing neural network classifier for diagnosis issues

Neural networks have been widely used in the field of intelligent information processing such as classification, clustering, prediction, and recognition. In this paper, a non-parametric supervised classifier based on neural networks is proposed for diagnosis issues. A parameter selection with self a...

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Vydáno v:	International journal of machine learning and cybernetics Ročník 4; číslo 3; s. 217 - 233
Hlavní autoři:	Barakat, M., Lefebvre, D., Khalil, M., Druaux, F., Mustapha, O.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer-Verlag 01.06.2013 Springer Nature B.V Springer
Témata:	Adaptive algorithms Artificial Intelligence Classification Classifiers Clustering Competition Complex Systems Computational Intelligence Control Data processing Engineering Engineering Sciences Fault detection Fault diagnosis Feature selection Genetic algorithms Mechatronics Neural networks Neurons Original Article Parameters Pattern Recognition Performance evaluation Random variables Robotics Scientific visualization Signal processing Subspaces Systems Biology Training Wavelet transforms Adaptive neural network Fault diagnosis Parameter selection
ISSN:	1868-8071, 1868-808X
On-line přístup:	Získat plný text
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