Exploring neuro-genetic processing of electronic nose data

Abstract

This paper explores neuro-genetic applications in processing electronic nose data corrupted with additive Gaussian noise. For this study, published sensor data for different polymer-coated surface-acoustic wave (SAW) sensor arrays exposed to fixed concentrations of hazardous vapours like diethyl sulphide (DES) and iso-octane (ISO) have been used. Dimensionality of resulting pattern recognition problem is varied by taking different numbers of sensors. We show that for low dimensionality instances of this problem, back-propagation performs adequately under noisy conditions. For high dimensionality instances, back-propagation has great difficulty in training the neural classifier even with repeated restarts and different weights initializations. To alleviate this problem, we propose use of a genetic algorithm with special MRX operator introduced by us and demonstrate very encouraging results with a genetically trained neural network model. © 1998 Elsevier Science Ltd. All rights reserved.