Adaptive Filter Theory

By Steve Rogers. 1996

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Citations in the corpus

Adaptive Orthogonal Projection for Batch and Online Continual Learning (2022, methodology): “…approximation of (cid:8) ( n ) from the statistics perspective to reduce the poor inﬂuence of (cid:11) , which is also inspired by the technique to approximate the correlation matrix (cid:8) ( n ) used in tracking the performance of RLS algorithm in non-stationary environments (Haykin 2008).”
Continual Learning with Recursive Gradient Optimization (2022, methodology): “the projection matrix Pl iteratively like recursive least square(RLS) (Haykin, 2002) algorithm at training step(see Appendix B.”
Enhancements to the Cumulant Method for Probabilistic Optimal Power Flow Studies (2009, methodology): “In order to calculate NSSE, sum of square error (SSE) is found using [19]”
The Matrix Cookbook (2006, background): “Notice: • Complex Gradient Vector: If f is a real function of a complex vector z , then the complex gradient vector is given by ([9, p. 798]) : If f is a real function of a complex matrix Z , then the complex gradient matrix is given by ([2]) These expressions can be used for gradient descent…”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, unknown): “The Wiener filter is an optimal linear decoder set to minimize mean-square error (347, 485, 854).”
Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations (2012, methodology): “filter because it applies the standard Kalman filter to a nonlinear system with additive white noise by continually updating a linearization around the previous state estimate [32].”
Constrained subpixel target detection for remotely sensed imagery (2000, methodology): “The idea of CEM arises from the MVDR in array processing [21], [22], with the desired target signature interpreted as the direction of arrival from a desired signal.”
Frequency domain equalization for single-carrier broadband wireless systems (2002, background): “2, a cyclic prefix of length P symbols is formed by reproducing the sequence of the last P transmitted symbols in a block, and adding this sequence to the beginning of the block before transmission.”
Pyroomacoustics: A Python Package for Audio Room Simulation and Array Processing Algorithms (2017, methodology): “The base class is extended to provide implementations of the least mean squares (LMS), normalized LMS (NLMS), and recursive least squares (RLS) [18].”
A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification (2008, background): “Moreover, the proposed algorithm has good tracking capabilities, a stable behavior, and it is very robust against different variations of the “corrupting signal.””
The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects (2007, methodology): “…the system automatically adapts to the specific brain signals of each user by employing advanced techniques of machine learning and signal processing (e.g., Müller et al., 2001; Haykin, 1995; and more specifically with respect to BCI: Blankertz et al., 2004, 2006c,d; Müller et al., 2003, 2004).”
Tissue Sensing Adaptive Radar for Breast Cancer Detection—Experimental Investigation of Simple Tumor Models (2005, methodology): “The method is adapted from a beamformer approach in [27].”
The kernel recursive least-squares algorithm (2004, methodology): “All tests were run on a 256Mb, 667MHz Pentium 3 Linux workstation.”
Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery Classification (2008, methodology): “of available estimation methods (for a review, refer to [24]) the Yule–Walker algorithm [20] was chosen to determine parameters of an AR model of a given order .”