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

ST. Sarkar, AP. Bhondekar, M. Macaš, R. Kumar, R. Kaur, A. Sharma, A. Gulati, A. Kumar,

. 2015 ; 71 (-) : 142-9. [pub] 20150824

Jazyk angličtina Země Spojené státy americké

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc16020305

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.

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$a The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis.
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$a Bhondekar, Amol P $u CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India. Electronic address: amol.bhondekar@gmail.com.
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$a Macaš, Martin $u Czech Technical University, Prague, Czech Republic.
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$a Kumar, Ritesh $u CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
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$a Kaur, Rishemjit $u CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
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$a Sharma, Anupma $u CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
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$a Gulati, Ashu $u CSIR-Institute of Himalayan Bioresource Technology, HP, India.
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$a Kumar, Amod $u CSIR-Central Scientific Instruments Organization, Chandigarh, India; Academy of Scientific and Innovative Research, New Delhi, India.
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