Title :
A fuzzy logic based neural network classifier for qualitative classification of odors/gases
Author :
Kumar, Ravi ; Das, R.R. ; Mishra, V.N. ; Dwivedi, R.
Author_Institution :
Dept. of Electron. Eng., Banaras Hindu Univ., Varanasi, India
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.
Keywords :
backpropagation; computerised instrumentation; electronic noses; fuzzy logic; fuzzy neural nets; intelligent sensors; pattern classification; sensor arrays; signal processing equipment; TRAINLM; backpropagation algorithm; fuzzy logic based neural network classifier; gas qualitative classification; odor discrimination; odor qualitative classification; thick film tin oxide sensor array; trained artificial neural network; Artificial neural networks; Backpropagation algorithms; Fuzzy logic; Gases; Logic arrays; Neural networks; Sensor arrays; Thick film sensors; Thick films; Tin; algorithm; electronic nose; fuzzy logic; intelligent gas sensor; microsensors; neural networks;
Conference_Titel :
Emerging Trends in Electronic and Photonic Devices & Systems, 2009. ELECTRO '09. International Conference on
Conference_Location :
Varanasi
Print_ISBN :
978-1-4244-4846-3
DOI :
10.1109/ELECTRO.2009.5441140