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- Publisher Website: 10.1109/ICNNSP.2003.1279361
- Scopus: eid_2-s2.0-78650115083
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Conference Paper: Identification of non-Gaussian parametric model with time-varying coefficients using wavelet basis
| Title | Identification of non-Gaussian parametric model with time-varying coefficients using wavelet basis |
|---|---|
| Authors | |
| Issue Date | 2003 |
| Publisher | IEEE. |
| Citation | International Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 659-662 How to Cite? |
| Abstract | Many time series in practice turn to be the time-varying (TV) non-Gaussian processes. In this paper, we address the problem of how to describe these non-stationary non-Gaussian time series. A non-Gaussian AR model with TV parameters is proposed to track the non-stationary non-Gaussian characteristics of the signal. Since wavelet has flexibility in capturing the signal's transient characteristics at different scales, a set of wavelet basis is employed so that the model parameters can effectively track the variations of TV signals and be used to estimate the corresponding TV bispectrum. The experiments results confirm the superior performance of the presented model over the previous method. |
| Persistent Identifier | http://hdl.handle.net/10722/46510 |
| ISBN |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Shen, MF | en_HK |
| dc.contributor.author | Zhang, YZ | en_HK |
| dc.contributor.author | Chan, FHY | en_HK |
| dc.date.accessioned | 2007-10-30T06:51:35Z | - |
| dc.date.available | 2007-10-30T06:51:35Z | - |
| dc.date.issued | 2003 | en_HK |
| dc.identifier.citation | International Conference on Neural Networks and Signal Processing Proceedings, Nanjing, China, 14-17 December 2003, v. 1, p. 659-662 | en_HK |
| dc.identifier.isbn | 0-7803-7702-8 | en_HK |
| dc.identifier.uri | http://hdl.handle.net/10722/46510 | - |
| dc.description.abstract | Many time series in practice turn to be the time-varying (TV) non-Gaussian processes. In this paper, we address the problem of how to describe these non-stationary non-Gaussian time series. A non-Gaussian AR model with TV parameters is proposed to track the non-stationary non-Gaussian characteristics of the signal. Since wavelet has flexibility in capturing the signal's transient characteristics at different scales, a set of wavelet basis is employed so that the model parameters can effectively track the variations of TV signals and be used to estimate the corresponding TV bispectrum. The experiments results confirm the superior performance of the presented model over the previous method. | en_HK |
| dc.format.extent | 195191 bytes | - |
| dc.format.extent | 13817 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.format.mimetype | text/plain | - |
| dc.language | eng | en_HK |
| dc.publisher | IEEE. | en_HK |
| dc.rights | ©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | - |
| dc.title | Identification of non-Gaussian parametric model with time-varying coefficients using wavelet basis | en_HK |
| dc.type | Conference_Paper | en_HK |
| dc.identifier.openurl | http://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-7803-7702-8&volume=1&spage=659&epage=662&date=2003&atitle=Identification+of+non-Gaussian+parametric+model+with+time-varying+coefficients+using+wavelet+basis | en_HK |
| dc.description.nature | published_or_final_version | en_HK |
| dc.identifier.doi | 10.1109/ICNNSP.2003.1279361 | en_HK |
| dc.identifier.scopus | eid_2-s2.0-78650115083 | - |
| dc.identifier.hkuros | 95141 | - |