Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

New filters for internal model control design

Abstract: In this paper we study the design of a new two-degree-of-freedom filter for the internal model control (IMC) method. The new filter alleviates some disadvantages of the standard IMC filter when the IMC method is applied to unstable plants that do not have non-minimum-phase zeros. We show that by employing the new filter, the resulting system has a flatter frequency response, better stability robustness, and little overshoot in the step response. Furthermore, one of its design parameters can be related directly to the closed-loop bandwidth and the other parameter can be used to control the recovery time after an overshoot has occurred in the step response. These features are important in the application of the IMC method to a new approach of adaptive robust control. Examples are given in the paper to illustrate the new filter design.

Kernel adaptive filtering under generalized Maximum Correntropy Criterion

Abstract: Owing to their universal approximation capability and online learning manner, kernel adaptive filters have been widely used in nonlinear systems modeling. Under Gaussian assumption, traditional kernel adaptive algorithms utilize the well-known mean square error(MSE) as a cost function to get optimal solutions. For non-Gaussian situations, MSE will not properly represent the statistics of the error, and hence degrade the performance. In recent years, an information theoretic learning(ITL) based criterion called Maximum Correntropy Criterion( MCC) has been proposed and applied in robust adaptive filtering. The correntropy is a generalized correlation measure in kernel space, which uses Gaussian kernel as a default kernel function. Of course, Gaussian kernel is not always the best choice. Recently, a more flexible definition of correntropy, called generalized correntropy, has been proposed. With a proper shape parameter, the generalized correntropy may get better performance than original correntropy with Gaussian kernel. In this paper, we take advantages of both kernel methods and generalized correntropy to develop a new kernel adaptive algorithm called Generalized Kernel Maximum Correntropy(GKMC) algorithm. We analyze theoretically the stability and steady-state performance of the new algorithm. In addition, we propose a Quantized GKMC(QGKMC) algorithm to curb the growth of the network size in GKMC while maintaining the performance. Simulation results confirm the theoretical expectations and show superior performance compared with existing methods.

A fast convergence frequency domain adaptive filter

Abstract: This correspondence presents a new fast convergence algorithm for frequency domain adaptive filter and its applicability to acoustic noise cancellation in speech signals.

Adaptive filter pitch extraction

Abstract: An enhancement system extracts pitch from a processed speech signal. The system estimates the pitch of voiced speech by deriving filter coefficients of an adaptive filter and using the obtained filter coefficients to derive pitch. The pitch estimation may be enhanced by using various techniques to condition the input speech signal, such as spectral modification of the background noise and the speech signal, and/or reduction of the tonal noise from the speech signal.