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River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia

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Format: Citation Index Journal
Published: 2012
Online Access:http://eprints.utp.edu.my/9589/
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id my.utp.eprints.9589
record_format eprints
spelling my.utp.eprints.95892013-04-02T05:24:53Z River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia 2012 Citation Index Journal NonPeerReviewed UNSPECIFIED (2012) River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia. [Citation Index Journal] http://eprints.utp.edu.my/9589/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
format Citation Index Journal
title River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
spellingShingle River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
title_short River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
title_full River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
title_fullStr River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
title_full_unstemmed River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms-A Case Study in Malaysia
title_sort river suspended sediment prediction using various multilayer perceptron neural network training algorithms-a case study in malaysia
publishDate 2012
url http://eprints.utp.edu.my/9589/
_version_ 1738655765104689152
score 13.214268

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