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...

Full description

Saved in:

Bibliographic Details
Published in:	International journal of machine learning and cybernetics Vol. 4; no. 3; pp. 217 - 233
Main Authors:	Barakat, M., Lefebvre, D., Khalil, M., Druaux, F., Mustapha, O.
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer-Verlag 01.06.2013 Springer Nature B.V Springer
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!