Performance of soft limiters in the LMS algorithm for cyclostationary white Gaussian inputs
| Autor: | Eweda Eweda, Jose C. M. Bermudez, Neil J. Bershad |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Cyclostationary process
Computer science Stochastic process Gaussian Monte Carlo method System identification 020206 networking & telecommunications 02 engineering and technology Power (physics) Least mean squares filter symbols.namesake Nonlinear system Control and Systems Engineering Signal Processing 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Algorithm Software |
| Zdroj: | Signal Processing. 152:197-205 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2018.05.023 |
| Popis: | The analysis of saturation-type nonlinearities on the input and the error in the weight update equation for LMS adaptation were obtained for a stationary white Gaussian data model in [28] for system identification. Here the input signal is modeled by a cyclostationary white Gaussian random process with periodically time-varying power. The system parameters vary according to a random-walk. Using the previous analysis results, nonlinear recursions are presented for the transient and steady-state weight first and second moments that include the effect of the soft limiters. Monte Carlo simulations of the algorithms provide strong support for the theory. |
| Databáze: | OpenAIRE |
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