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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yongsheng Wang Wenzhi Jiang Shengzhi Yuan Jianguo Wang |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Armament Sci. & Technol., Navy Aeronaut. Eng. Univ., Yantai (Yongsheng Wang; Wenzhi Jiang; Shengzhi Yuan; Jianguo Wang) |
| Abstract | The chaotic time series forecast was researched by using wavelet neural networks (WNN) in this paper. An improved training method for WNN was presented. The method combines the genetic algorithm (GA) with gradient descent BP algorithm; the BP method was embedded in the GA operation in order to resolve the GA's limitation in detail search capability. In the last step of the method the WNN searches the best solution using BP method once again. The experiment on predicting the chaotic time series from Henon map illustrates the performance of the method; the experimental result also shows the method can assure the WNN convergence quickly and have the higher forecasting precision. |
| Starting Page | 519 |
| Ending Page | 523 |
| File Size | 407540 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533049 |
| DOI | 10.1109/ICNC.2008.283 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Chaos Wavelet domain chaos forecasting Artificial neural networks time series Entropy Genetic algorithms Design optimization Chaotic communication Neural networks Signal processing algorithms genetic arithmetic (GA) Technology forecasting wavelet neural networks (WNN) |
| Content Type | Text |
| Resource Type | Article |
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