Titlebook: Efficient Nonlinear Adaptive Filters; Design, Analysis and Haiquan Zhao,Badong Chen Book 2023 The Editor(s) (if applicable) and The Author(

變色龍

https://doi.org/10.1007/978-3-642-91457-7rfered with by non-Gaussian noise. Therefore, an adaptive spline filtering algorithm based on MCC criterion is proposed (SAF-MCC). At the same time, the steady-state performance of SAF-MCC algorithm is analyzed. Simulation results demonstrate the advantages of the SAF-MCC algorithm. Finally, taking

Rodent

Volterra Adaptive Filter,ol, and so on. With the wide application of adaptive networks, distributed adaptive learning algorithms for nonlinear adaptive networks have become a hot spot. In this chapter, we introduce a robust diffusion Volterra (DV) algorithm for distributed network nonlinear system identification in the pres

致命

FLANN Adaptive Filter,les of FLANNs and several improved models, such as the recursive FLANN model, and the convex combination of FLANN filters is presented. The nonlinear characteristics of FLANN structure are verified by computer simulation, and the control effects of several introduced robust algorithms based on FLANN

捕鯨魚(yú)叉

Sputum

Adaptive Filter,ive algorithm is the key part to determine the performance of filter. This chapter introduces the commonly used linear and nonlinear adaptive filters and their related algorithms and gives the detailed derivation process of the classical LMS, RLS, and AP algorithms; subband filtering algorithm; and

完成

Volterra Adaptive Filter,ich makes the linear filter theory not applicable in these cases. In order to solve this problem, nonlinear filter came into being. Among nonlinear filters, the Volterra filter based on polynomial expansion is the most popular, which can model a class of nonlinear systems with appropriate accuracy.

植物群

Thymus

Spline Adaptive Filter,ly a linear-nonlinear network. Several algorithms based on spline filter are also introduced. This chapter introduces the SAF-LMS algorithm based on the least mean square (LMS). However, its update process is affected by the eigenvalue spread of the autocorrelation matrix of the input signal. Theref

AWE

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