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| Paper: | SPTM-P1.9 |
| Session: | System Identification and Parameter Estimation |
| Time: | Tuesday, May 18, 13:00 - 15:00 |
| Presentation: |
Poster |
| Topic: |
Signal Processing Theory and Methods: System Modeling, Representation, & Identification |
| Title: |
MINIMUM ENTROPY ESTIMATION AS A NEAR MAXIMUM-LIKELIHOOD METHOD AND ITS APPLICATION IN SYSTEM IDENTIFICATION WITH NON-GAUSSIAN NOISE |
| Authors: |
Minh Ta; University of Oklahoma | | |
| | Victor DeBrunner; University of Oklahoma | | |
| Abstract: |
We derive the Minimum Entropy Estimation (MEE) method from Information Theory to show the similarity of this method to the Maximum Likelihood Method for the linear regression problem. The result is a nonparametric-based identification technique that can be applied in any case with iid noise that outperforms estimators in this case, including the popular LS method and a recently-developed (and limited) version of the MEE. Performance-wise, the MEE method is comparable to the Expectation-Maximization (EM) method. Its application to FIR system identification produces a very efficient implementation of this technique. |
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