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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jifu Nong |
| Copyright Year | 2011 |
| Description | Author affiliation: College of Mathematics and Computer Science, Guangxi University for Nationalities, Nanning, China (Jifu Nong) |
| Abstract | One of the biggest problems in designing or training RBF neural networks are the overfitting problem. The traditional design of RBF neural networks may be pursued in a variety of ways. In this paper, we present a method for the design of RBF networks to solve overfitting problem. For a practical application, frequency information is usually available for designing RBF networks by frequency domain analysis, which has a sound mathematical basis. We try to include the frequency information into the design of RBF networks, which achieve the task of approximated a function in certain frequency range and have the property of structural risk minimization. After the structure of designed network is determined, the linear weights of the output layer are the only set of adjustable parameters. The approach of design is verified by approximation cases. |
| File Size | 662953 |
| File Format | |
| ISBN | 9781612842752 |
| e-ISBN | 9781612842769 |
| DOI | 10.1109/ICEOE.2011.6013050 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-29 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Atmospheric modeling Radial basis function networks Predictive models Matrix decomposition Frequency domain analysis Spectral analysis |
| Content Type | Text |
| Resource Type | Article |
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