Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články
PubMed
26356597
DOI
10.1016/j.neunet.2015.07.014
PII: S0893-6080(15)00151-3
Knihovny.cz E-zdroje
- Klíčová slova
- Dynamically evolving spiking neural networks, Electronic nose, McNemar’s test, Spike latency coding, Spiking neural network, Tea,
- MeSH
- algoritmy MeSH
- biomimetika MeSH
- čaj * MeSH
- čichová percepce MeSH
- design vybavení MeSH
- elektronický nos * MeSH
- neuronové sítě * MeSH
- normální rozdělení MeSH
- nos MeSH
- odoranty * MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- čaj * MeSH
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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