Efficient learning in neural networks
In studies of neural networks, the Multilavered Feedforward Network is the most widely used network architecture while the Backpropagation (BP)algo-rithm is the prime learning algorithm for this kind of networks. Although the BP algorithm is thought to be based on a solid theoretical background, som...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/19615 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In studies of neural networks, the Multilavered Feedforward Network is the most widely used network architecture while the Backpropagation (BP)algo-rithm is the prime learning algorithm for this kind of networks. Although the BP algorithm is thought to be based on a solid theoretical background, some of its drawbacks hamper its use in solving problems efficiently. These draw-backs include slow convergence, the problem of local minima, and the degra-dation in performance due to practical constraints such as limited weight precision in real implementation of the algorithm. |
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