The Performance Analysis of Extreme Learning Machines on Odour Recognition
Küçük Resim Yok
Tarih
2018
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ASSOC COMPUTING MACHINERY
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
2nd International Conference on Cloud and Big Data Computing (ICCBDC) / 7th International Conference on Intelligent Information Processing (ICIIP) -- AUG 03-05, 2018 -- Barcelona, SPAIN
Anahtar Kelimeler
Extreme Learning Machine, Machine olfaction, MOS sensors, Artificial neural network
Kaynak
PROCEEDINGS OF 2018 2ND INTERNATIONAL CONFERENCE ON CLOUD AND BIG DATA COMPUTING (ICCBDC 2018)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
