<i>Adaptive Filters; Structures, Algorithms, and Applications</i> by M. Honig and D. Messerschmitt

Honig, Michael L.; Messerschmitt, David G.; Kirsteins, Ivars; Quazi, Azizul H.

doi:10.1121/1.394896

Cited by 54 publications

(87 citation statements)

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“…Alternately, a sign-sign update formula (7) will require only an addition operation to update Equations (5) and (7) are the stochastic gradient adaptive lattice algorithm for normalized lattice filters. In the literature, there are also two types of stochastic gradient lattice algorithms for un-normalized lattice filters [10]. Regardless of which of these adaptive algorithms are used, however, none of them admit practical realization using a CORDIC-based arithmetic unit.…”

Section: Sg Adaptive Lattice Algorithmsmentioning

confidence: 99%

“…A distinct feature of CALF is that it requires only a simple up/down binary counter per lattice stage to realize the stochastic gradient (SG)-based adaptation algorithm. This compares very favorably, in terms of cost and ease of design, to the microprocessor-based architecture used in conventional implementation of adaptive lattice algorithms [4], [8]- [10], [14], [18]. The simplicity and regularity of the CALF architecture makes it an excellent candidate for many adaptive lattice filter applications such as echo cancellation or channel equalization.…”

Section: Introductionmentioning

confidence: 99%

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On the convergence of the CORDIC adaptive lattice filtering (CALF) algorithm

1998

IEEE Trans. Signal Process.

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Abstract-In this paper, the convergence of a recently proposed CORDIC adaptive lattice filtering (CALF) algorithm is proved. It is shown that the update of the rotation angle (which is equivalent to the reflection coefficient) can be modeled by the state transition of a regular Markov chain, with each rotation angle being a state. The convergence of the CALF algorithm then is established as this Markov chain converges from an initial state probability distribution to its limiting state probability distribution. Formulae that enable explicit calculation of the limiting state distribution are derived. Moreover, it is shown that the algorithm has an exponential convergence rate.

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Section: Sg Adaptive Lattice Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the convergence of the CORDIC adaptive lattice filtering (CALF) algorithm

1998

IEEE Trans. Signal Process.

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“…Examples of this are the surveillance, recording, and playback equipment. There are several classes of adaptive filtering (Honig & Messerschmitt, 1984) that can be useful for speech enhancement, as will be shown in Sect. 4.…”

Section: Multichannel Adaptive Filtersmentioning

confidence: 99%

Multichannel Speech Enhancement

Garcia¹,

Torres-Guijarro²

2008

New Developments in Robotics Automation and Control

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“…In th.is paper we will develop an Adaptive Momentum Bac.kpropagation (AMBP) algorithm, whon improved optimization technique leads to scaling relatio:nahips in learning mo.re favourable than Tuauro and Janssen's o,nes (7]. Our algorithm is baaed on three choices:…”

Section: "mentioning

confidence: 99%

“…The type of adaptation of the units in a neural network, is very similar to the adaptation laws that are used for the LMS algorithm used for adaptive filtering [6], [7], in the following paragraph we will show how to adapt the idea of a Newton search to the context of neural nets on the hypothesis that the data at the input of each unit has a gausian statistic and there is incorrclation between the elements of the input vector.…”

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confidence: 99%