| Full text | |
| Author(s): |
Total Authors: 4
|
| Affiliation: | [1] Fed Univ ABC, Santo Andre, SP - Brazil
[2] Univ Brasilia, Brasilia, DF - Brazil
[3] Univ Estadual Campinas, Campinas, SP - Brazil
Total Affiliations: 3
|
| Document type: | Journal article |
| Source: | ELECTRONICS LETTERS; v. 55, n. 15, p. 859+, JUL 25 2019. |
| Web of Science Citations: | 0 |
| Abstract | |
In supervised infinite impulse response adaptive filtering, approximate gradient-based approaches are the usual option among optimisation methods. When based on the mean squared error (MSE) criterion, however, these approaches may present biased solutions in noisy scenarios. In that sense, instead of the MSE, the authors propose the use of Shannon's error entropy, an information theoretic learning criterion, which is able to extract higher order statistics from the underlying signals. In particular, a multivariate entropy definition is considered, which is applied to derive a Recursive Prediction Error-based algorithm. The performance analyses are carried out in the context of the supervised channel equalisation problem, with results very favourable to the proposal, in high and low noise level environments. (AU) | |
| FAPESP's process: | 13/14185-2 - New Methods for Adaptive Equalization Based on Information Theoretic Learning |
| Grantee: | Denis Gustavo Fantinato |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 17/11488-5 - Multivariate Analysis of the Data Temporal Structure for Blind Source Separation in the Context of Nonlinear Mixtures and of Multiple Datasets |
| Grantee: | Denis Gustavo Fantinato |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |