Performance of the Levenberg–Marquardt neural network training method in electronic nose applications

Abstract

The focus of this study is to find the appropriateness of the Levenberg–Marquardt (LM) neural network (NN) training algorithm for recognition of odor patterns associated with an electronic nose (e-nose). Multiple time-patterns represent step response of the array of sensors to the odorants. The experiments are performed on four representative classes of odorants: coffees, fragrances, hog farm air, and cola beverages. The odor recognition system is composed of a Karhunen–Loéve (KL) based pre-processing unit, and a feedforward neural network with the LM training algorithm. The parameters of the pre-processing unit and the neural network are fine-tuned using a genetic algorithm. Back-propagation algorithm with adaptive learning rate is selected as a standard neural network training method, for the purpose of comparison. The results of the experiments indicate that the LM algorithm provides high correct recognition ratios. In addition, the results confirm that the LM method outperforms the back-propagation (BP) method with adaptive learning rate, for the classes of the odorants provided in this study.