UNSPECIFIED (1999) Fuzzy ARTMAP based electronic nose data analysis. SENSORS AND ACTUATORS B-CHEMICAL, 61 (1-3). pp. 183-190. ISSN 0925-4005

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Abstract

The Fuzzy ARTMAP neural network is a supervised pattern recognition method based on fuzzy adaptive resonance theory (ART). It is a promising method since Fuzzy ARTMAP is able to carry out on-line learning without forgetting previously learnt patterns (stable learning), it can recode previously learnt categories (adaptive to changes in the environment) and is self-organising. This paper presents the application of Fuzzy ARTMAP to odour discrimination with electronic nose (EN) instruments. EN data from three different datasets, alcohol, coffee and cow's breath (in order of complexity) were classified using Fuzzy ARTMAP. The accuracy of the method was 100% with alcohol, 97% with coffee and 79%, respectively. Fuzzy ARTMAP outperforms the best accuracy so far obtained using the back-propagation trained multilayer perceptron (MLP) (100%, 81% and 68%, respectively). The MLP bring by far the most popular neural network method in both the field of EN instruments and elsewhere. These results, in the case of alcohol and coffee, are better than those obtained using self-organising maps, constructive algorithms and other ART techniques. Furthermore, the time necessary to train Fuzzy ARTMAP was typically one order of magnitude faster than back-propagation. The results show that this technique is very promising for developing intelligent EN equipment, in terms of its possibility for on-line learning, generalisation ability and ability to deal with uncertainty (in terms of measurement accuracy, noise rejection, etc.). (C) 1999 Elsevier Science S.A. All rights reserved.

Item Type:	Journal Article
Subjects:	Q Science > QD Chemistry
Journal or Publication Title:	SENSORS AND ACTUATORS B-CHEMICAL
Publisher:	ELSEVIER SCIENCE SA
ISSN:	0925-4005
Date:	14 December 1999
Volume:	61
Number:	1-3
Number of Pages:	8
Page Range:	pp. 183-190
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/13788

Data sourced from Thomson Reuters' Web of Knowledge

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