Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding
One 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 Des...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
School of Engineering, Taylor’s University College
2007
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/6463/ http://irep.iium.edu.my/6463/ http://irep.iium.edu.my/6463/1/188-_199_Mahmoud.pdf |
| Summary: | One 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. |
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