On the design issue of intelligent electronic nose system

Abstract

Intelligent electronic nose (ENOSE) system technology is gaining importance in several industrial applications. These include process control and quality control in industries such as foodstuffs, beverages, tobacco, perfumery and pharmaceutical. ENOSE is also crucial component in industrial safety (smoke and hazardous gas detection) as well as environmental pollution control. This paper deals with design of an intelligent ENOSE system for the identification of gas/odours using a sensor array and a neural network pattern classifier. Previous researchers have shown that the power of discrimination increases rapidly with the number of sensors in the array whose information potential is very large and the pattern recognition (PARC) method is a clever way to extract this information. The authors show in this paper with the powerful PARC technique, the need of larger array can be compensated. With this view, they design a neural classifier using two different learning approaches and train the network over the responses of surface acoustic wave (SAW) sensors exposed to hazardous vapours like diethyl sulphide (DES) and iso-octane (ISO). Dimensionality of the data set is varied from 1 to 8 by taking different number of sensors. It is found that for a backpropagation trained neural classifier, the optimum number of sensors required for satisfactory classification under noisy conditions is 4 to 5. This is a very limited range beyond which backpropagation has great difficulty in training the neural classifier even with repeated restarts and different weight initializations. To alleviate this problem, hybridization of soft computing tools like neural networks and genetic algorithms promises to provide the design of better intelligent system. The authors propose the use of a genetic algorithm based on a special MRX operator introduced by them and demonstrate very encouraging results with genetically trained neural network model even with larger as well as smaller numbers of sensors.