Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification

Nurul Shakila, Ahmad Zubir and Mohamad Aqib Haqmi, Abas and Ismail, N. A. and Nor Azah, Mohd Ali and Mohd Hezri, Fazalul Rahiman and Ng, K. M. and Saiful Nizam, Tajuddin (2017) Analysis of algorithms variation in multilayer perceptron neural network for agarwood oil qualities classification. In: 2017 IEEE 8th Control and System Graduate Research Colloquium, ICSGRC 2017 , 4 - 5 August 2017 , Grand Blue Wave Hotel, Shah Alam. pp. 122-126. (8070580). ISBN 9781538603802

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Abstract

This study investigates the performance of the Multilayer Perceptron (MLP) classifier in discriminating the qualities of agarwood oil significant compounds by different qualities based on three training algorithms namely Scaled Conjugate Gradient (SCG), Levernbergh-Marquardt (LM) and Resilient Backpropagation (RP) Neural Network by using Matlab version 2013a. The dataset used in this study were obtained at Forest Research Institute Malaysia (FRIM) and University Malaysia Pahang (UMP). Further, the areas (abundances, %) of chemical compounds is set as an input and the quality represented (high or low) as an output. The MLP performance was examined with different number of hidden neurons which is in the ranged of 1 to 10. Their performances were observed to accurately found the best technique of optimization to apply to the model. It was found that the LM is effective in reducing the error by enhancing the number of hidden neurons during the network development. The MSE of LM is the smallest among SCG and RP. Besides that, the accuracy of training, validation and testing of LM performed the best accuracy (100%).

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Levernbergh-Marquardt (LM); Forest Research Institute Malaysia (FRIM); Scaled Conjugate Gradient (SCG); Resillient Backpropagation (RP)
Subjects:	Q Science > QA Mathematics Q Science > QD Chemistry T Technology > TP Chemical technology
Faculty/Division:	Faculty of Industrial Sciences And Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	21 Mar 2022 07:12
Last Modified:	21 Mar 2022 07:12
URI:	http://umpir.ump.edu.my/id/eprint/28990
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