Topic
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.
Papers published on a yearly basis
Papers
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01 Jan 1998
164 citations
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10 Apr 1978TL;DR: An adaptive filter structure which may be used in multi-channel noise-cancelling applications that incorporates a lattice filter framework, rather than tapped-delay-lines, which offers advantages in adaptive convergence rate which cannot be achieved with tapped- delay-lines.
Abstract: This paper describes an adaptive filter structure which may be used in multi-channel noise-cancelling applications. The proposed structure differs from those presented previously in that it incorporates a lattice filter framework, rather than tapped-delay-lines. The successive orthogonalization provided by the lattice offers advantages in adaptive convergence rate which cannot be achieved with tapped-delay-lines. In the sections below, we present an explicit description of the general noise-cancelling lattice structure, together with the appropriate adaptive algorithms.
163 citations
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TL;DR: It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise, and the TLS solution is seen to produce unbiased solutions.
Abstract: An algorithm for recursively computing the total least squares (TLS) solution to the adaptive filtering problem is described. This algorithm requires O(N) multiplications per iteration to effectively track the N-dimensional eigenvector associated with the minimum eigenvalue of an augmented sample covariance matrix. It is shown that the recursive least squares (RLS) algorithm generates biased adaptive filter coefficients when the filter input vector contains additive noise. The TLS solution on the other hand, is seen to produce unbiased solutions. Examples of standard adaptive filtering applications that result in noise being added to the adaptive filter input vector are cited. Computer simulations comparing the relative performance of RLS and recursive TLS are described. >
162 citations
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TL;DR: Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithms, which are applied to nonlinear communication channel equalization problems.
Abstract: Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come from human experts or are determined during the adaptation procedure by matching input-output data pairs; (3) construct a filter based on the set of rules; and (4) update the free parameters of the filter using the RLS algorithm. The design procedure for the LMS fuzzy adaptive filter is similar. The most important advantage of the fuzzy adaptive filters is that linguistic information (in the form of fuzzy IF-THEN rules) and numerical information (in the form of input-output pairs) can be combined in the filters in a uniform fashion. The filters are applied to nonlinear communication channel equalization problems. >
161 citations
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TL;DR: This paper presents a structure adaptive anisotropic filtering technique with its application to processing magnetic resonance images that differs from other techniques in that, instead of using local gradients as a means of controlling the anisotropism of filters, it uses both a local intensity orientation and ananisotropic measure of level contours to control the shape and extent of the filter kernel.
161 citations