Rice yield prediction - a comparison between enhanced back propagation learning algorithms
Back Propagation algorithm(BP} has been popularly used to solve various problems, however it is shrouded with the problems of low convergence and instability. In recent years, improvements have been attempted to overcome the discrepancies aforementioned. In this study, we examine the performance of...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2004
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| Subjects: | |
| Online Access: | http://eprints.utm.my/3410/ http://eprints.utm.my/3410/1/aryati_-_Rice_Yield_Prediction_-_A_Comparison_between_Enhanced_Back_Propagation_Learning.pdf |
| Summary: | Back Propagation algorithm(BP} has been popularly used to solve various problems, however it is shrouded with the problems of low convergence and instability. In recent years, improvements have been attempted to overcome the discrepancies aforementioned. In this study, we examine the performance of four enhanced BP algorithms to predict rice yield in MADA plantation area in Kedah, Malaysia. Amidst the four algorithms explored, Conjugate Gradient Descent exhibits the best performance. |
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