Accelerating learning performance of back propagation algorithm by using adaptive gain together with adaptive momentum and adaptive learning rate on classification problems
The back propagation (BP) algorithm is a very popular learning approach in feedforward multilayer perceptron networks. However, the most serious problem associated with the BP is local minima problem and slow convergence speeds. Over the years, many improvements and modifications of the back...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7955/1/J3714_e277896270c61202b64daf13c7d0f992.pdf http://eprints.uthm.edu.my/7955/ https://doi.org/10.1007/978-3-642-20998-7_62 |
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| Summary: | The back propagation (BP) algorithm is a very popular learning
approach in feedforward multilayer perceptron networks. However, the most
serious problem associated with the BP is local minima problem and slow
convergence speeds. Over the years, many improvements and modifications of
the back propagation learning algorithm have been reported. In this research,
we propose a new modified back propagation learning algorithm by introducing
adaptive gain together with adaptive momentum and adaptive learning rate into
weight update process. By computer simulations, we demonstrate that the
proposed algorithm can give a better convergence rate and can find a good
solution in early time compare to the conventional back propagation. We use
two common benchmark classification problems to illustrate the improvement
in convergence time. |
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