231 research outputs found
Adaptive Mixture Methods Based on Bregman Divergences
We investigate adaptive mixture methods that linearly combine outputs of
constituent filters running in parallel to model a desired signal. We use
"Bregman divergences" and obtain certain multiplicative updates to train the
linear combination weights under an affine constraint or without any
constraints. We use unnormalized relative entropy and relative entropy to
define two different Bregman divergences that produce an unnormalized
exponentiated gradient update and a normalized exponentiated gradient update on
the mixture weights, respectively. We then carry out the mean and the
mean-square transient analysis of these adaptive algorithms when they are used
to combine outputs of constituent filters. We illustrate the accuracy of
our results and demonstrate the effectiveness of these updates for sparse
mixture systems.Comment: Submitted to Digital Signal Processing, Elsevier; IEEE.or
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce a novel family of adaptive filtering algorithms based on a
relative logarithmic cost. The new family intrinsically combines the higher and
lower order measures of the error into a single continuous update based on the
error amount. We introduce important members of this family of algorithms such
as the least mean logarithmic square (LMLS) and least logarithmic absolute
difference (LLAD) algorithms that improve the convergence performance of the
conventional algorithms. However, our approach and analysis are generic such
that they cover other well-known cost functions as described in the paper. The
LMLS algorithm achieves comparable convergence performance with the least mean
fourth (LMF) algorithm and extends the stability bound on the step size. The
LLAD and least mean square (LMS) algorithms demonstrate similar convergence
performance in impulse-free noise environments while the LLAD algorithm is
robust against impulsive interferences and outperforms the sign algorithm (SA).
We analyze the transient, steady state and tracking performance of the
introduced algorithms and demonstrate the match of the theoretical analyzes and
simulation results. We show the extended stability bound of the LMLS algorithm
and analyze the robustness of the LLAD algorithm against impulsive
interferences. Finally, we demonstrate the performance of our algorithms in
different scenarios through numerical examples.Comment: Submitted to IEEE Transactions on Signal Processin
Stochastic analysis of an error power ratio scheme applied to the affine combination of two LMS adaptive filters
The affine combination of two adaptive filters that simultaneously adapt on the same inputs has been actively investigated. In these structures, the filter outputs are linearly combined to yield a performance that is better than that of either filter. Various decision rules can be used to determine the time-varying parameter for combining the filter outputs. A recently proposed scheme based on the ratio of error powers of the two filters has been shown by simulation to achieve nearly optimum performance. The purpose of this paper is to present a first analysis of the statistical behavior of this error power scheme for white Gaussian inputs. Expressions are derived for the mean behavior of the combination parameter and for the adaptive weight mean-square deviation. Monte Carlo simulations show good to excellent agreement with the theoretical predictions
An affine combination of two LMS adaptive filters - Transient mean-square analysis
This paper studies the statistical behavior of an affine combination of the outputs of two LMS adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor is restricted to the interval . The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the MSE. First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSD's of either filter
Plant identification via adaptive combination of transversal filters
For least mean-square (LMS) algorithm applications, it is important to improve the speed of convergence vs the residual error trade-off imposed by the selection of a certain value for the step size. In this paper, we propose to use a mixture approach, adaptively combining two independent LMS filters with large and small step sizes to obtain fast convergence with low misadjustment during stationary periods. Some plant identification simulation examples show the effectiveness of our method when compared to previous variable step size approaches. This combination approach can be straightforwardly extended to other kinds of filters, as it is illustrated with a convex combination of recursive least-squares (RLS) filters.Publicad
Combinations of adaptive filters
Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target localization, and tracking. A trending approach in this direction is to recur to in-network distributed processing in which individual nodes implement adaptation rules and diffuse their estimation to the network [4], [5].The work of Jerónimo Arenas-García and Luis Azpicueta-Ruiz was partially supported by the Spanish Ministry of Economy and Competitiveness (under projects TEC2011-22480 and PRI-PIBIN-2011-1266. The work of Magno M.T. Silva was partially supported by CNPq under Grant 304275/2014-0 and by FAPESP under Grant 2012/24835-1. The work of Vítor H. Nascimento was partially supported by CNPq under grant 306268/2014-0 and FAPESP under grant 2014/04256-2. The work of Ali Sayed was supported in part by NSF grants CCF-1011918 and ECCS-1407712. We are grateful to the colleagues with whom we have shared discussions and coauthorship of papers along this research line, especially Prof. Aníbal R. Figueiras-Vidal
Convex combination of adaptive filters for a variable tap-length LMS algorithm
A convex combination of adaptive filters is utilized
to improve the performance of a variable tap-length
least-mean-square (LMS) algorithm in a low signal-to-noise
environment (SNR 0 dB). As shown by our simulations,
the adaptation of the tap-length in the variable tap-length LMS
algorithm is highly affected by the parameter choice and the noise
level. Combination approaches can improve such adaptation by
exploiting advantages of parallel adaptive filters with different
parameters. Simulation results support the good properties of the
proposed method
Introducing Adaptive filters Based on Shadow Concept for Speech Processing
This paper presents the new approach to introducing adaptive Filter with LMS Algorithm based on Shadow concept. Which is useful for the cancellation of the noise component overlap with Speech signal in the same frequency range, but fixed LMS algorithm produces minimum convergence rate and fixed steady state error. So we presents design, implementation and performance of adaptive FIR filter, based on Shadow concept, which produces minimum mean square error compare to fixed LMS, and we also obtains denoised Speech signal at output, and also we propose to calculate SNR values of Adaptive Filter with LMS algorithm with and without Shadow concept
Diffusion Adaptation over Networks under Imperfect Information Exchange and Non-stationary Data
Adaptive networks rely on in-network and collaborative processing among
distributed agents to deliver enhanced performance in estimation and inference
tasks. Information is exchanged among the nodes, usually over noisy links. The
combination weights that are used by the nodes to fuse information from their
neighbors play a critical role in influencing the adaptation and tracking
abilities of the network. This paper first investigates the mean-square
performance of general adaptive diffusion algorithms in the presence of various
sources of imperfect information exchanges, quantization errors, and model
non-stationarities. Among other results, the analysis reveals that link noise
over the regression data modifies the dynamics of the network evolution in a
distinct way, and leads to biased estimates in steady-state. The analysis also
reveals how the network mean-square performance is dependent on the combination
weights. We use these observations to show how the combination weights can be
optimized and adapted. Simulation results illustrate the theoretical findings
and match well with theory.Comment: 36 pages, 7 figures, to appear in IEEE Transactions on Signal
Processing, June 201
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