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Title: The Performance Analysis of Extreme Learning Machines on Odour Recognition
Authors: Eşme, Engin
Kıran, Mustafa Servet
Keywords: Extreme Learning Machine
Machine Olfaction
Mos Sensors
Artificial Neural Network
Electronic Nose
Issue Date: 2018
Abstract: Extreme Learning Machine (ELM) is a single hidden layer feed-forward neural network learning method, which has a high generalization performance as well as faster. In this paper, odour data is discriminated based on the sensor response curve by using ELM, and the main objective is to investigate the optimum number of nodes in the hidden layer of ELM for olfactory detection. The relationship between the number of nodes in the hidden layer and the number of attributes or classes of dataset is queried to achieve the goal. Three odour datasets taken from different sources in literature and two transfer functions for the ELM are used to verify the results of the study. The backpropagation (BP) algorithm is also used for training an artificial neural network for comparison purposes. The analysis is performed for the three datasets by using ELM and BP and obtained results present that the time consumption of ELM is too small to be compared with BP even though the number of nodes is high and better accuracy rates are obtained by ELM.
Description: 2nd International Conference on Cloud and Big Data Computing (ICCBDC) / 7th International Conference on Intelligent Information Processing (ICIIP) -- AUG 03-05, 2018 -- Barcelona, SPAIN
ISBN: 978-1-4503-6474-4
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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