Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Performance ranking of artificial neural network learning algorithms in solar radiation forecast
Artificial Neural Networks (ANNs) area prormsmg 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...
Saved in:
| Format: | Conference Proceeding |
|---|---|
| Published: |
2010
|
| Tags: |
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000njm a2000000a 4500 | ||
|---|---|---|---|
| 001 | oai:repository.ui.edu.ng:123456789/2137 | ||
| 042 | |a dc | ||
| 720 | |a Fadare, D. A. |e author | ||
| 720 | |a Asafa, T. B. |e author | ||
| 260 | |c 2010 | ||
| 520 | |a Artificial Neural Networks (ANNs) area prormsmg 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 neuron(s) 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. 7.4° 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. | ||
| 024 | 8 | |a ui_inpro_fadare_performance_2010 | |
| 024 | 8 | |a Proceedings of Nigerian Institute of Industrial Engineers 2010 Conference, pp. 104-112 | |
| 024 | 8 | |a http://ir.library.ui.edu.ng/handle/123456789/2137 | |
| 245 | 0 | 0 | |a Performance ranking of artificial neural network learning algorithms in solar radiation forecast |