A Self-Adjusting Simple Hyperstable Adaptive Recursive Algorithm
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Hyperstability theory when simplified and adapted for digital signal processing offers a new class of IIR filters, simpl...
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| Published in: | International journal of modelling & simulation Vol. 27; no. 2; pp. 164 - 170 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Calgary
Taylor & Francis
2007
Taylor & Francis Ltd |
| Subjects: | |
| ISSN: | 0228-6203, 1925-7082 |
| Online Access: | Get full text |
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| Summary: | Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Hyperstability theory when simplified and adapted for digital signal processing offers a new class of IIR filters, simple hyperstable adaptive recursive filters (SHARFs), which is directly related to strictly positive real (SPR) transfer functions. One of the most important drawbacks of the SHARF algorithm is the presence of the unknown denominator in the transfer function that must be SPR in order to ensure convergence. In this paper, SHARF is investigated with SPR transfer functions designed without any priori knowledge of the filter parameters by the pole-zero placement on the unit circle method and made self-adjusting. To demonstrate self-adjustment of the algorithm, SHARF algorithm using constraint least-mean square (LMS) method is applied to a pure four-pole autoregressive process. |
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| Bibliography: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| ISSN: | 0228-6203 1925-7082 |
| DOI: | 10.1080/02286203.2007.11442413 |