A fuzzy logic based neural network classifier for qualitative classification of odors/gases

Abstract

This paper presents a novel approach to odor discrimination using data obtained from the responses of thick film tin oxide sensor array fabricated at our laboratory and employing backpropagation algorithm trained artificial neural network based on fuzzy logic. Fuzzy membership values were used as target vectors to the proposed neural classifier. Three different versions of backpropagation algorithm were used to train the network and their performances have been compared. Superior learning and classification performance was obtained using proposed model trained with TRAINLM version of the backpropagation algorithm.

Fuzzy Logic based Odour Classification System in Electronic Nose

“…Therefore, the gas classifier designed as stated above shows a classification accuracy of 93.75 %. When the classification of odours was investigated using back propagation neural network, the simulation using matlab function TRAINLM showed an efficiency of 100% [14]. In the experiment performed by Meegahapola et.al, for the same set of gases, fuzzy logic approach gives around 94% accuracy and it is close to 97% for the neural network based approach [8].…”