An Adaptive Prediction Algorithm Based on Maximum Correntropy Criterion

Abstract

The traditional cost function, minimization mean square prediction error is a second order statistic, and it is based on the error Gaussian distribution and linear assumption. But chaotic signals are non-Gaussian, so the optimization criterion is not suitable. Then we present using the robust optimization criterion, maximum correntropy to replace the popular minima mean square error criterion minimization error. In simulation, the algorithm shows an improved performance to a common three-order Volterra prediction.