Learning algorithm effect on multilayer feed forward artificial neural network performance in image coding

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dc.contributor.authorMahmoud, Omer
dc.contributor.authorAnwar, Farhat
dc.contributor.authorSalami, Momoh-Jimoh E.
dc.date.accessioned2019-08-14T14:23:49Z
dc.date.available2019-08-14T14:23:49Z
dc.date.issued2007-08
dc.description.abstractOne of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms.en_US
dc.identifier.citationMahmoud, O., Anwar, F., & Salami, M. J. E. (2007). Learning algorithm effect on multilayer feed forward artificial neural network performance in image coding. Journal of Engineering Science and Technology, 2(2), 188-199.en_US
dc.identifier.urihttp://repository.elizadeuniversity.edu.ng/handle/20.500.12398/477
dc.language.isoenen_US
dc.publisherJournal of Engineering Science and Technologyen_US
dc.subjectImage Compression /Decompressionen_US
dc.subjectNeural Networken_US
dc.subjectOptimisationen_US
dc.titleLearning algorithm effect on multilayer feed forward artificial neural network performance in image codingen_US
dc.typeArticleen_US
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