Chapter 5

Maximum Correntropy Criterion–Based Kernel Adaptive Filters

Badong Chen; Xin Wang; Jose C. Principe,    Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, ChinaUniversity of Florida, Gainesville, FL, United States


Kernel adaptive filters (KAFs) are a family of powerful online kernel learning methods that can learn nonlinear and nonstationary systems efficiently. Most of the existing KAFs are obtained by minimizing the well-known mean square error (MSE). The MSE criterion is computationally simple and very easy to implement but may suffer from a lack of robustness to non-Gaussian noises. To deal with the non-Gaussian noises robustly, the optimization criteria must go beyond the ...

Get Adaptive Learning Methods for Nonlinear System Modeling now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.