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.
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References
Gomez-Fernandez M, Wong W-K, Tokuhiro A, Welter KM, Alhawsawi A, Yang H, Highley K (2021) Isotope identification using deep learning: an explanation. Nucl Instrum Methods Phys Res Sect Accelerators, Spectrometers, Detectors and Associated Equip
Karkra S, Singh P, Kaur, Sharma R (2021) Deep learning architectures: a hierarchy in convolution neural network technologies. Adv Electromech Technol 439–457
Rosin T, Romano M, Keedwell E, Kapelan Z (2021) A committee evolutionary neural network for the prediction of combined sewer overflows. Water Resour Manag
Seref B, Bostanci E, Guzel M (2021) Evolutionary neural networks for improving the prediction performance of recommender systems. Turkish J Electr Eng Comput Sci 62–77
Sultan Mohd M, Johari J, Ruslan F (2020) Application of NNARX in modeling a solar radiation prediction. In: IEEE 8th conference on systems, process and control (ICSPC), Malacca, Malaysia, pp 225–229
Samadianfard S, Hashemi S, Kargar K, Izadyar M, Mostafaeipour A, Mosavi A, Shamshirband S (2020) Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm. Energy Reports, pp 1147–1159
Kim P (2020) MATLAB deep learning: with machine learning, neural networks and artificial intelligence. Republic of Korea
Mehranzamir K, Abdul-Malek Z, Afrouzi H, Mashak S, Wooi C-I, Zarei R (2020) Artificial neural network application in an implemented lightning location system. J Atmosp Solar-Terr Phys
Bilski J, Kowalczyk B, Marchlewska A, Zurada J (2020) Local levenberg-marquardt algorithm for learning feedforward neural networks. J Artif Intell Soft Comput Res 10(4):229–316
Rafati J, Marica R (2020) Quasi-newton optimization methods for deep learning applications. Deep Learn Appl 9–38
Zhou W (2020) A modified BFGS type quasi-newton method with line search for symmetric nonlinear equations problems. J Comput Appl Math
Anushka P, Md A, Upaka R (2020) Comparison of different Artificial Neural Network (ANN) training algorithms to predict the atmospheric temperature in Tabuk, Saudi Arabia. Q J Meteorol Hydrol Geophys 71
Schneider M (2020) A dynamical view of nonlinear conjugate gradient methods with applications to FFT-based computational micromechanics. Comput Mech 66:239–257
Lin Q, Leandro J, Gerber S, Disse M (2020) Multistep flood inundation forecasts with resilient backpropagation neural networks: Kulmbaci case study. Water 12
Ghorpede V, Koneru V (2020) Pattern recognition neural network model for experimental based compressive strength graded self compacting concrete. Mater Today Proc
Inavov B, Ma H, Mosic D (2020) A survey of gradient methods for solving nonlinear optimization. Electr Res Archive 1573–1624
Gao T, Gong X, Zhang K, Lin F, Wang J, Huang T, Zurada J (2020) A recalling-enhanced recurrent neural network: conjugate gradient learning algorithm and its convergence analysis. Inf Sci 519:273–288
Cao J, Wu J (2020) A conjugate gradient learning algorithm and its applications in image restoration. Appl Numer Math 152:243–252
Zhang K, Liu H, Liu Z (2021) A new adaptive subspace minimization three-term conjugate gradient algorithm for unconstrained optimization. J Comput Math
Al-Bashir A, Al-Dweri M, Al-Ghandoor A, Hammad B, Al-Kouz W (2020) Analysis of effects of solar irradiance, cell temperature and wind speed on photovoltaic systems performance. Int J Energy Econ Policy 10:353–359
Acknowledgements
This work is supported by the Research Management Center Universiti Teknologi MARA Shah Alam under Sustainable Research Grant (Project Code: 600-RMC/SRC/5/3 (047/2020)). Authors would also like to thank and acknowledge the Innovative Electromobility Research Laboratory (ITEM) and School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA Shah Alam for their support.
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Mohd, M.R.S., Johari, J., Ruslan, F.A., Razak, N.A., Ahmad, S., Shah, A.S.M. (2022). Analysis of Training Function for NNARX in Solar Radiation Prediction Modeling. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_47
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DOI: https://doi.org/10.1007/978-981-16-8515-6_47
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