Heuristic and Bio-inspired Neural Network Model

Abstract

E-nose technology for detecting indoor harmful gases and concentration estimation of harmful gases and estimating the concentration become feasible by using a multi-sensor system. The estimation accuracy in actual application is concerned too much bymanufacturers and researchers. This chapter analyzes the application of different bio-inspired and heuristic techniques to improve the concentration estimation in experimental electronic nose application. In this chapter, seven different particle swarm optimization models are studied including six models used for 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 present 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.