ABSTRACT
Artificial Neural Networks (ANNs) area prormsing alternative to conventional tools in
modeling and prediction of complex and non-linear parameters. However, the selection of
appropriate network parameters for optimum performance pose application challenges In this
study, the modeling and predictive performances of six backpropagation learning algorithms:
Levenberg-Marquardt (LM), BFGS Quasi-Newton (BFG), Resilient Backpropagation (RP),
Fletcher-Powell Conjugate Gradient (CGF), Variable Learning Rate Backpropagation (GDX)
and Bayesian Reglarization (BR) in solar radiation forecast were investigated.
Multilayer perceptron (MPL) neural network with five, ten and one nc.uronfs) in the input,
hidden and output layers, respectively was designed with MATLAB® neural network toolkit
and trained with the six learning algorithms using the daily global solar radiation data of
Ibadan (Lat. 7.40 N; Long. 3.90 E; Alt. 227.2m), Nigeria. The network performance was
ranked based on the number of iterations required for convergence, and coefficient of
correlation (r-value), mean square error (MSE) and mean absolute percentage error (MAPE)
between the actual and predicted values of the training and testing datasets. Results showed
that the LM and BR learning algorithms are the two best algorithms to be considered for use
in modeling and forecasting of solar radiation data.
Fadar, D , Olugasa, T & Falana, A (2021). Performance Ranking Of Artificial Neural Network Learning Algorithlvis In Solar Radiatio!\ Forecast. Afribary. Retrieved from https://afribary.com/works/performance-ranking-of-artificial-neural-network-learning-algorithlvis-in-solar-radiatio-forecast
Fadar, D. et. al. "Performance Ranking Of Artificial Neural Network Learning Algorithlvis In Solar Radiatio!\ Forecast" Afribary. Afribary, 20 Apr. 2021, https://afribary.com/works/performance-ranking-of-artificial-neural-network-learning-algorithlvis-in-solar-radiatio-forecast. Accessed 26 Dec. 2024.
Fadar, D., T. Olugasa and A. Falana . "Performance Ranking Of Artificial Neural Network Learning Algorithlvis In Solar Radiatio!\ Forecast". Afribary, Afribary, 20 Apr. 2021. Web. 26 Dec. 2024. < https://afribary.com/works/performance-ranking-of-artificial-neural-network-learning-algorithlvis-in-solar-radiatio-forecast >.
Fadar, D. , Olugasa, T. and Falana, A. . "Performance Ranking Of Artificial Neural Network Learning Algorithlvis In Solar Radiatio!\ Forecast" Afribary (2021). Accessed December 26, 2024. https://afribary.com/works/performance-ranking-of-artificial-neural-network-learning-algorithlvis-in-solar-radiatio-forecast