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Authors:
El Khatir Haimoudi
Loubna Cherrat
Otman Abdoun
Mostafa Ezziyyani
Keywords: Pattern recognitions; Artificial Neural Network; self-organizing map; preliminary processing of input vectors; Data visualising; principal component analysis; power iteration algorithm
Abstract:
Self-Organizing Map (SOM) is a very effective solution for solving pattern recognition problems. However, some ambiguities appear during learning process with the existence of linear patterns in the learning data, in this case, the learning process lasts for a long time and the network produces irrelevant results. The work provides the resolution of the detected problem and the application of the SOM for the pattern recognition. To achieve our objective and minimize the learning time, a SOM improved model has been developed. This model uses a special block able to filter the input data and reduce the size of the learning multitude. The presented experimental test results in this work show that the improved model exceeds the standard model in terms of the recognition results accuracy and the learning time. The results obtained in this work encouraged us to think about using the improved model to develop a smart approach (SmartMaps) of Geographic Information Systems (GIS).
Pages: 38 to 44
Copyright: Copyright (c) IARIA, 2016
Publication date: May 22, 2016
Published in: conference
ISSN: 2326-9332
ISBN: 978-1-61208-477-0
Location: Valencia, Spain
Dates: from May 22, 2016 to May 26, 2016