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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

  • Czech description

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

Result continuities

  • Project

  • 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

  • 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