Reduced Complexity Volterra Models for Nonlinear System Identification
<p/> <p>A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2001-01-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1155/S1110865701000324 |
| _version_ | 1818253682465046528 |
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| author | Hacıoğlu Rıfat Williamson Geoffrey A |
| author_facet | Hacıoğlu Rıfat Williamson Geoffrey A |
| author_sort | Hacıoğlu Rıfat |
| collection | DOAJ |
| description | <p/> <p>A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET) within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex) pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.</p> |
| first_indexed | 2024-12-12T16:43:57Z |
| format | Article |
| id | doaj.art-024250b35f3d42f89b31bbc12f692d10 |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-12-12T16:43:57Z |
| publishDate | 2001-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-024250b35f3d42f89b31bbc12f692d102022-12-22T00:18:30ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802001-01-0120014734913Reduced Complexity Volterra Models for Nonlinear System IdentificationHacıoğlu RıfatWilliamson Geoffrey A<p/> <p>A broad class of nonlinear systems and filters can be modeled by the Volterra series representation. However, its practical use in nonlinear system identification is sometimes limited due to the large number of parameters associated with the Volterra filter′s structure. The parametric complexity also complicates design procedures based upon such a model. This limitation for system identification is addressed in this paper using a Fixed Pole Expansion Technique (FPET) within the Volterra model structure. The FPET approach employs orthonormal basis functions derived from fixed (real or complex) pole locations to expand the Volterra kernels and reduce the number of estimated parameters. That the performance of FPET can considerably reduce the number of estimated parameters is demonstrated by a digital satellite channel example in which we use the proposed method to identify the channel dynamics. Furthermore, a gradient-descent procedure that adaptively selects the pole locations in the FPET structure is developed in the paper.</p>http://dx.doi.org/10.1155/S1110865701000324nonlinear system identificationvolterra model structureorthonormal basis functions |
| spellingShingle | Hacıoğlu Rıfat Williamson Geoffrey A Reduced Complexity Volterra Models for Nonlinear System Identification EURASIP Journal on Advances in Signal Processing nonlinear system identification volterra model structure orthonormal basis functions |
| title | Reduced Complexity Volterra Models for Nonlinear System Identification |
| title_full | Reduced Complexity Volterra Models for Nonlinear System Identification |
| title_fullStr | Reduced Complexity Volterra Models for Nonlinear System Identification |
| title_full_unstemmed | Reduced Complexity Volterra Models for Nonlinear System Identification |
| title_short | Reduced Complexity Volterra Models for Nonlinear System Identification |
| title_sort | reduced complexity volterra models for nonlinear system identification |
| topic | nonlinear system identification volterra model structure orthonormal basis functions |
| url | http://dx.doi.org/10.1155/S1110865701000324 |
| work_keys_str_mv | AT hac305o287lur305fat reducedcomplexityvolterramodelsfornonlinearsystemidentification AT williamsongeoffreya reducedcomplexityvolterramodelsfornonlinearsystemidentification |