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Rice Yield prediction - a comparison between Enchanced 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...

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書誌詳細
主要な著者: Puteh, Saad, Nor Khairah, Jamaludin, Nursalasawati, Rusli, Aryati, Bakri, Siti Sakira, Kamarudin
フォーマット: 論文
言語:English
出版事項: Universiti Malaysia Perlis 2009
主題:
Back-Propagation algorithm
Quick Propagation
Rice yield prediction
Conjugate gradient descent
Algorithms
Backpropagation network
Back propagation
オンライン･アクセス:http://dspace.unimap.edu.my/xmlui/handle/123456789/6982
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要約: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 MAD A plantation area in Kedah, Malaysia. A midst the four algorithms explored, Conjugate Gradient Descent exhibits the best performance.

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