Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images

Al-Rawi, Kamal (2001). Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images. Thesis (Doctoral), Facultad de Informática (UPM).

Description

Title: Design new supervised art-type artificial neural networks, and their performances for classification landsat TM images
Author/s:
  • Al-Rawi, Kamal
Contributor/s:
  • Gonzalo Martín, Consuelo
Item Type: Thesis (Doctoral)
Date: 2001
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

New Supervised ART ANNs with simple architectures have been developed in this study. Their architectures have been built from a single module of ART rather than a pair of them connected by a map field as all other supervised ART-type ANNs that have been reported in the literatee. Two different algorithms have been developed: Supervised ART-I and Supervised ART-II. The developed algorithms reduced the number of dynamic parameters, memory requirement, and the training time which is the major problem facing the ANNs, without altering the classification accuracy. Two simplified versión of Fuzzy ART algorithms have been developed, keeping the categorization performance as that of the original algorithm. They are Flagged Fuzzy ART and Compact Fuzzy ART. While Supervised ART-I and Supervised ART-II are general in nature that can be applied to all ART ANNs, the supervisión of Compact Fuzzy ART has been addressed in this work. The full algorithms for Supervised ART-I and Supervised ART-II have been listed. The newly developed ANNs have been applied to classify Landsat Thematic Mapper (TM) images. The performance of the systems has been tested for different dynamic parameters and different training samples. The behavior of the systems in the vigilance parameter and dynamic learning parameter space has been addressed. Their performances in the domain of the vigilance parameter and the dynamic learning parameter have been understood. Only one approach, for vigilance dynamic in all supervised ART-type ANNs, has been addressed in the literatee. Three more approaches have been developed in this study: fixed, free, and float. The performance of the developed ANNs for classification landsat TM images has been tested for all these different vigilance dynamics.

More information

Item ID: 42680
DC Identifier: http://oa.upm.es/42680/
OAI Identifier: oai:oa.upm.es:42680
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 11 Jul 2016 12:59
Last Modified: 11 Jul 2016 12:59
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