Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/586
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 Prediction Perception Sensor Juice |
Publisher: | ASSOC COMPUTING MACHINERY | 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 | URI: | https://doi.org/10.1145/3264560.3264575 https://hdl.handle.net/20.500.13091/586 |
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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