Analysis of training function for NNARX in solar radiation prediction modeling

Mohd Rizman, Sultan Mohd and Juliana, Johari and Fazlina, Ahmat Ruslan and Noorfadzli, Abdul Razak and Salmiah, Ahmad and Ahmad Syahiman, Mohd Shah (2022) Analysis of training function for NNARX in solar radiation prediction modeling. In: Lecture Notes in Electrical Engineering; 8th International Conference on Computational Science and Technology, ICCST 2021 , 28-29 August 2021 , Virtual, Online. pp. 619-632., 835 (275849). ISSN 1876-1100 ISBN 978-981168514-9

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Abstract

Neural Network Autoregressive Model with Exogenous Input (NNARX) is one of the Evolutionary Neural Network that had been used to develop a prediction model for solar radiation which involves a dynamic non-linear for a time-series based prediction. The whole process of Training, Testing, and Validation of NNARX is carried out by using a Training Function. Since there are several improvements that had been made in enhanced the prediction results using NNARX, it is the best to setup an analysis on the Training Function in finding the best to suit the solar radiation prediction modeling. In this paper, the analysis of the Training Function algorithm for solar radiation prediction modeling development using NNARX is carried out using MATLAB R2019a software. Each Training Function algorithm will be used in modeling development and their prediction output will be compared with the actual output. Based on the results, it is shown that Levenberg–Marquardt Training Function is the best Training Function algorithm for NNARX in solar radiation prediction modeling with the coefficient of determination value, R2 of 0.93423.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	MATLAB; Neural network; NNARX; Solar radiation prediction; Training function
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	College of Engineering Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	01 Dec 2023 08:12
Last Modified:	01 Dec 2023 08:12
URI:	http://umpir.ump.edu.my/id/eprint/39472
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