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dc.contributor.authorAbu Nasir, Mohammad Enamul Kabir
dc.contributor.authorHussain Muhammad Imran, Hasan
dc.contributor.authorMohd Abdur Rashid, Dr.
dc.contributor.authorAzralmukmin, Azmi
dc.contributor.authorMd. Zakir, Hossain
dc.contributor.authorMd. Shahjahan
dc.date.accessioned2014-04-21T08:17:49Z
dc.date.available2014-04-21T08:17:49Z
dc.date.issued2013-10
dc.identifier.citationAmerican Journal of Applied Sciences, vol. 10(10), 2013, pages 1172-1180en_US
dc.identifier.issn1546-9239
dc.identifier.urihttp://thescipub.com/abstract/10.3844/ajassp.2013.1172.1180
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/33889
dc.descriptionLink to publisher's homepage at http://thescipub.com/en_US
dc.description.abstractRain fall and Temperature are undoubtedly two important factors that balance water in the environment. Adequate study of the rain behavior helps to forecast it. The time series obtained from different stations of the country throughout the several years are collected and analyzed. The dynamics of rain fall time series is analyzed with Correlation Dimension (CD) to characterize the several zones of Bangladesh. In addition a Neural Network (NN) predictor model was designed to realize complexity of rain fall. We found the interesting similarity between CD and NN predictor. The findings are useful in explaining why several zones show behavioral regularity and change.en_US
dc.language.isoenen_US
dc.publisherScience Publicationen_US
dc.subjectComplexityen_US
dc.subjectLearning and predictionen_US
dc.subjectNeural networken_US
dc.subjectRain fallen_US
dc.subjectTime series analysisen_US
dc.titleResemblance of rain fall in Bangladesh with correlation dimension and neural network learningen_US
dc.typeArticleen_US
dc.contributor.urlabdurrashid@unimap.edu.myen_US
dc.contributor.urlazralmukmin@unimap.edu.myen_US


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