On Comparison between Radial Basis Function and Wavelet Basis Functions Neural Networks
In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate g...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2017-05-01
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| Series: | Ibn Al-Haitham Journal for Pure and Applied Sciences |
| Online Access: | https://jih.uobaghdad.edu.iq/index.php/j/article/view/968 |
| Summary: | In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the output space. The trained networks are then used in an conjugate gradient algorithm to estimate the output. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented
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| ISSN: | 1609-4042 2521-3407 |