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Novel Sparse Algorithms Based on Lyapunov Stability for Adaptive System Identification
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pogula, Rakesh Kumar, T. Kishore Albu, Felix |
| Copyright Year | 2018 |
| Abstract | Adaptive filters are extensively used in the identification of an unknown system. Unlike several gradientsearch based adaptive filtering techniques, the Lyapunov Theory-based Adaptive Filter offers improved convergence and stability. When the system is described by a sparse model, the performance of Lyapunov Adaptive (LA) filter is degraded since it fails to exploit the system sparsity. In this paper, the Zero-Attracting Lyapunov Adaptation algorithm (ZA-LA), the Reweighted Zero-Attracting Lyapunov Adaptation algorithm (RZA-LA) and an affine combination scheme of the LA and proposed ZA-LA filters are proposed. The ZA-LA algorithm is based on l1-norm relaxation while the RZA-LA algorithm uses a log-sum penalty to accelerate convergence when identifying sparse systems. It is shown by simulations that the proposed algorithms can achieve better convergence than the existing LMS/LA filter for a sparse system, while the affine combination scheme is robust in identifying systems with variable sparsity. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.radioeng.cz/fulltexts/2018/18_01_0270_0280.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Adaptive filter Adaptive system Algorithm Convergence (action) Gilles de la Tourette syndrome Gramática de la lengua castellana Least mean squares filter Linear programming relaxation Lyapunov fractal Simulation Sparse matrix System identification Taxicab geometry ZoneAlarm |
| Content Type | Text |
| Resource Type | Article |
