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Title: Development and application of an enhanced ART-Based neural network
Authors: Keem, Siah Yap
Chee, Peng Lim
W.M Lee, Eric
Junita, Mohamed Saleh
???metadata.dc.contributor.url???: keemsiayap@yahoo.com
Keywords: Adaptive resonance theory;Generalized regression neural network;Rule extraction;Fire safety engineering;Neural networks (Computer science);Fire prevention;Neural computers
Issue Date: 11-Oct-2009
Publisher: Universiti Malaysia Perlis
Citation: p.5B8 1 - 5B8 6
Series/Report no.: Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009)
Abstract: 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.
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.
URI: http://hdl.handle.net/123456789/7316
Appears in Collections:Conference Papers

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