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Training Neural Networks Using Input Data CharacteristicsCERNAZANU, C.![]() ![]() ![]() |
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Author keywords
neural networks, data mining, correlation-based feature subset selection method, data features extraction, training algorithm
References keywords
neural(8), networks(7), data(7), selection(6), learning(6), mining(5), machine(5), ijcnn(4), feature(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2008-06-02
Volume 8, Issue 2, Year 2008, On page(s): 65 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.02012
Web of Science Accession Number: 000264815000012
SCOPUS ID: 77955635511
Abstract
Feature selection is often an essential data processing step prior to applying a learning algorithm. The aim of this paper consists in trying to discover whether removal of irrelevant and redundant information improves the performance of neural network training results. The present study will describe a new method of training the neural networks, namely, training neural networks using input data features. For selecting the features, we used a filtering technique (borrowed from data mining) which consists in selecting the best features from a training set. The technique is made up of two components: a feature evaluation technique and a search algorithm for selecting the best features. When applied as a data preprocessing step for one common neural network training algorithms, the best data results obtained from this network are favorably comparable to a classical neural network training algorithms. Nevertheless, the first one requires less computation. |
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[1] Automatic and Parallel Optimized Learning for Neural Networks performing MIMO Applications, FULGINEI, F. R., LAUDANI, A., SALVINI, A., PARODI, M., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 13, 2013.
Digital Object Identifier: 10.4316/AECE.2013.01001 [CrossRef] [Full text]
[2] Enhancement of microgrid dynamic responses under fault conditions using artificial neural network for fast changes of photovoltaic radiation and FLC for wind turbine, Rezvani, Alireza, Izadbakhsh, Maziar, Gandomkar, Majid, Energy Systems, ISSN 1868-3967, Issue 4, Volume 6, 2015.
Digital Object Identifier: 10.1007/s12667-015-0156-6 [CrossRef]
[3] A novel sensorless field oriented controller for Permanent Magnet Synchronous Motors, Aygun, Hilmi, Gokdag, Mustafa, Aktas, Mustafa, Cernat, Mihai, 2014 IEEE 23rd International Symposium on Industrial Electronics (ISIE), ISBN 978-1-4799-2399-1, 2014.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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