Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification

Sarkar, S.T. and Bhondekar, A.P. and Macaš, Martin and Kumar, Ritesh and Kaur, Rishemjit and Sharma, Anupma and Gulati, Ashu and Kumar, Amod (2015) Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification. Neural Networks, 71. pp. 142-149. ISSN 0893-6080

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Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.

Item Type: Article
Uncontrolled Keywords: Electronic nose; McNemar’s test; Spiking neural network; Tea; Spike latency coding; Dynamically evolving spiking neural networks
Subjects: CSIO > Agri - Instrumentation
Divisions: Agri - Instrumentation
Depositing User: Ms. Jyotsana
Date Deposited: 03 May 2017 12:53
Last Modified: 03 May 2017 12:53
URI: http://csioir.csio.res.in/id/eprint/641

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