Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F15%3A00231222" target="_blank" >RIV/68407700:21730/15:00231222 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.neunet.2015.07.014" target="_blank" >http://dx.doi.org/10.1016/j.neunet.2015.07.014</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.neunet.2015.07.014" target="_blank" >10.1016/j.neunet.2015.07.014</a>
Alternative languages
Result language
angličtina
Original language name
Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Networks
ISSN
0893-6080
e-ISSN
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Volume of the periodical
71
Issue of the periodical within the volume
november
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
8
Pages from-to
142-149
UT code for WoS article
000364160900013
EID of the result in the Scopus database
2-s2.0-84941094207