Chaos-Based Neural Network Optimization Approach

Abstract

E-nose combined with a pattern recognition module can be used for estimating gases concentration. This chapter introduces the concentration estimations of indoor contaminants using chaos-based optimization artificial neural network integrated into our E-nose instrument. Back-propagation neural network (BPNN) has been the common pattern recognition algorithm for E-nose; however, it has local optimal flaw. This chapter presents a novel chaotic sequence optimization BPNN method. Experimental results demonstrate the superiority and efficiency of the portable E-nose instrument integrated into chaos-based artificial neural network optimization algorithms in real-time monitoring of air quality in dwellings.