| 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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