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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ying Li Jiun-Ming Deng |
| Copyright Year | 1998 |
| Description | Author affiliation: Dept. of Electr. Eng., Yuan Ze Univ., Taoyuan, Taiwan (Ying Li) |
| Abstract | A weight adaptation algorithm for normalized radial basis function networks is discussed. This simple algorithm-WAV-is based on the idea of Weighted AVeraging, and is inspired by the functional equivalence of normalized radial basis function networks and probabilistic fuzzy inference systems. We compare WAV with other weight adaptation algorithms including least mean square algorithm (LMS) and recursive least square algorithm (RLS) for tuning the weights of a normalized radial basis function network in the time series prediction problem with Box and Jenkins' data. Simulation shows that WAV achieves close to RLS mean square error with computation complexity close to LMS. Further study is suggested. |
| Starting Page | 117 |
| Ending Page | 120 |
| File Size | 351390 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780350081 |
| DOI | 10.1109/ICECS.1998.814845 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1998-09-07 |
| Publisher Place | Portugal |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Inference algorithms Radial basis function networks Least squares approximation Resonance light scattering Fuzzy neural networks Fuzzy systems Least mean square algorithms Least squares methods Computational modeling Mean square error methods |
| Content Type | Text |
| Resource Type | Article |
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