Gases concentration estimation using heuristics and bio-inspired optimization models for experimental chemical electronic nose

Abstract

By virtue of an electronic nose, detection and concentration estimation of harmful gases indoor become feasible by using a multi-sensor system. The estimation accuracy in actual application is constantly aspired by manufactures and researchers. This paper analyzes the application of different bio-inspired and heuristic techniques to the problem of concentration estimation in experimental electronic nose application. In this paper, seven different particle swarm optimization models are considered including six models used before in numerical function optimization, and a novel hybrid model of particle swarm optimization and adaptive genetic algorithm, for optimizing back-propagation multilayer perceptron neural network. We describe the performance of a particle swarm optimization technique, an adaptive genetic strategy and a back-propagation artificial neural network approach to perform concentration estimation of chemical gases and improve the intelligence of an E-nose.