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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The performance of the PNN structure was discussed based on the experimental results.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, the binary gas mixtures were classified using probabilistic neural network (PNN).
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Key Findings & Data:A data set consisted of the steady state sensor responses from the quartz crystal microbalance (QCM) type sensors were used for the training of the PNN.
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Conclusion & Impact:The performance of the PNN structure was discussed based on the experimental results.
🔬Design:Article
📍Scope:Comprehensive Sample
⚙️Variables:gas mixtures · probabilistic neural network · classification
Abstract
In this study, the binary gas mixtures were classified using probabilistic neural network (PNN). A data set consisted of the steady state sensor responses from the quartz crystal microbalance (QCM) type sensors were used for the training of the PNN. The performance of the PNN structure was discussed based on the experimental results.
Özet
Bu çalismada, ikili gaz karisimlari olasiliksal sinir agi (PNN) kullanilarak siniflandirilmistir. Kuatz kristal mikrobalans (QCM) tipi sensorlarden elde edilen kararli hal sensor cevaplarini içeren bir veri seti PNN’nin egitimi için kullanilmistir. PNN yapisinin performansi deneysel sonuçlara dayanarak tartisilmistir
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Cite This Article
Yusubov, İ., A., A., Temurtas, F. (2007). A Study on Mixture Classification Using Neural Network. Electronic Letters on Science and Engineering, Volume: 3 Issue: 2. https://ajindex.com/articles/1181112