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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Networks

ISSN

0893-6080

e-ISSN

—

Svazek periodika

71

Číslo periodika v rámci svazku

november

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

142-149

Kód UT WoS článku

000364160900013

EID výsledku v databázi Scopus

2-s2.0-84941094207