Kernel LMS - Pokharel, P. P.; Weifeng Liu; Principe, J. C. (2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007)
In this paper a nonlinear adaptive algorithm based on a kernel space least mean squares (LMS) approach is presented. With most of the neural network based methods for time series modeling it is difficult to implement a sample-by-sample adaptation method. This puts a serious limitation on the applicability of adaptive nonlinear filters in many optimal signal processing and communication applications where data arrives sequentially. This paper shows that the kernel LMS algorithm provides a computational simple and an effective algorithm to train nonlinear systems for system modeling without the need for regularization, without convergence to local minima and without the need for a separate book of data as a training set.