A comparative study of non-MSE criteria in nonlinear equalization
Levy Boccato, Daniel G. Silva, Denis G. Fantinato, Rafael Ferrari, Romis Attux · 2014
This work studies the application of non-MSE criteria to adapt the linear readout of Extreme Learning Machines (ELMs) in the context of communication channel equalization. A qualitative and experimental analysis is performed, in terms of bit error rate, optimization surface and decision boundary. The results reached by the ELM-based equalizer, considering three different noise models, did not reveal clear advantages of using criteria based on the concepts of error entropy, correntropy, and the L1-norm of the error. Notwithstanding, the observed results motivate a theoretical investigation on the conditions under which the potential discrepancies between the optimal solutions of these criteria may be stressed.