Adaptive System Identification using Markov Chain Monte Carlo
One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown system. This is accomplished by placing a kno...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2015-01-01.
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| LEADER | 02554 am a22002773u 4500 | ||
|---|---|---|---|
| 001 | ijeecs4184_3545 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Anjum, Muhammad Ali Raza |e author |
| 100 | 1 | 0 | |e contributor |
| 245 | 0 | 0 | |a Adaptive System Identification using Markov Chain Monte Carlo |
| 260 | |b Institute of Advanced Engineering and Science, |c 2015-01-01. | ||
| 520 | |a One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown system. This is accomplished by placing a known system in parallel and feeding both systems with the same input. Due to initial disparity in their impulse responses, an error is generated between their outputs. This error is set to tune the impulse response of known system in a way that every change in impulse response reduces the magnitude of prospective error. This process is repeated until the error becomes negligible and the responses of both systems match. To specifically minimize the error, numerous adaptive algorithms are available. They are noteworthy either for their low computational complexity or high convergence speed. Recently, a method, known as Markov Chain Monte Carlo (MCMC), has gained much attention due to its remarkably low computational complexity. But despite this colossal advantage, properties of MCMC method have not been investigated for adaptive system identification problem. This article bridges this gap by providing a complete treatment of MCMC method in the aforementioned context. DOI: http://dx.doi.org/10.11591/telkomnika.v13i1.6925 | ||
| 540 | |a Copyright (c) 2015 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |a Technology; Electrical, Electronics and Computer Engineering, Adaptive Signal Processing | ||
| 690 | |a Markov chain, Monte Carlo, System identification, Wiener-Hopf, Adaptive filter | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 1: January 2015; 124-136 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v13.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/4184/3545 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/4184/3545 |z Get fulltext |