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Development and application of an enhanced ART-Based neural network

Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.

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Bibliographic Details
Main Authors: Keem, Siah Yap, Chee, Peng Lim, W.M Lee, Eric, Junita, Mohamed Saleh
Other Authors: keemsiayap@yahoo.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis 2009
Subjects:
Adaptive resonance theory
Generalized regression neural network
Rule extraction
Fire safety engineering
Neural networks (Computer science)
Fire prevention
Neural computers
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7316
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spelling my.unimap-73162010-01-21T01:45:10Z Development and application of an enhanced ART-Based neural network Keem, Siah Yap Chee, Peng Lim W.M Lee, Eric Junita, Mohamed Saleh keemsiayap@yahoo.com Adaptive resonance theory Generalized regression neural network Rule extraction Fire safety engineering Neural networks (Computer science) Fire prevention Neural computers Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. The Generalized Adaptive Resonance Theory (GART) neural network is developed based on an integration of Gaussian ARTMAP and the Generalized Regression Neural Network. As in our previous work [13], GART is capable of online learning and is effective in tackling both classification and regression tasks. In this paper, we further propose an Ordered–Enhanced GART (EGART) network with pruning and rule extraction capabilities. The new network, known as O–EGART–PR, is equipped with an ordering algorithm that determines the sequences of training samples, a Laplacian function, a new vigilance function, a new match-tracking mechanism, and a rule extraction procedure. The applicability of O–EGART–PR to pattern classification and rule extraction problems is evaluated with a problem in fire dynamics, i.e., to predict the occurrences of flashover in a compartment fire. The outcomes demonstrate that O–EGART–PR outperforms other networks and produces meaningful rules from data samples. 2009-11-17T08:33:17Z 2009-11-17T08:33:17Z 2009-10-11 Working Paper p.5B8 1 - 5B8 6 http://hdl.handle.net/123456789/7316 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Adaptive resonance theory
Generalized regression neural network
Rule extraction
Fire safety engineering
Neural networks (Computer science)
Fire prevention
Neural computers
spellingShingle Adaptive resonance theory
Generalized regression neural network
Rule extraction
Fire safety engineering
Neural networks (Computer science)
Fire prevention
Neural computers
Keem, Siah Yap
Chee, Peng Lim
W.M Lee, Eric
Junita, Mohamed Saleh
Development and application of an enhanced ART-Based neural network
description Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
author2 keemsiayap@yahoo.com
author_facet keemsiayap@yahoo.com
Keem, Siah Yap
Chee, Peng Lim
W.M Lee, Eric
Junita, Mohamed Saleh
format Working Paper
author Keem, Siah Yap
Chee, Peng Lim
W.M Lee, Eric
Junita, Mohamed Saleh
author_sort Keem, Siah Yap
title Development and application of an enhanced ART-Based neural network
title_short Development and application of an enhanced ART-Based neural network
title_full Development and application of an enhanced ART-Based neural network
title_fullStr Development and application of an enhanced ART-Based neural network
title_full_unstemmed Development and application of an enhanced ART-Based neural network
title_sort development and application of an enhanced art-based neural network
publisher Universiti Malaysia Perlis
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7316
_version_ 1643788776161411072
score 13.222552

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