The Experts below are selected from a list of 81708 Experts worldwide ranked by ideXlab platform
Cedric Richard - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Behavior of the nonnegative least mean fourth algorithm for stationary Gaussian inputs and slow learning
Signal Processing, 2016Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to be estimated due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow one to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis. HighlightsThe nonnegative least mean fourth algorithm is proposed for the online estimation.This paper analyzes the Stochastic Behavior of the nonnegative least mean fourth algorithm.The Stochastic Behavior analysis is presented by using new approximations.Simulation results illustrate the accuracy of the proposed analysis.
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Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
arXiv: Numerical Analysis, 2015Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.
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ICASSP - A Stochastic Behavior analysis of Stochastic restricted-gradient descent algorithm in reproducing kernel hilbert spaces
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Masa-aki Takizawa, Masahiro Yukawa, Cedric RichardAbstract:This paper presents a Stochastic Behavior analysis of a kernel-based Stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It also includes stability conditions in the mean and mean-square sense. The present study is based on the analysis of the kernel normalized least mean square (KNLMS) algorithm initially proposed by Chen et al. Simulation results validate the analysis.
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A Stochastic Behavior analysis of Stochastic restricted-gradient descent algorithm in reproducing kernel Hilbert spaces
arXiv: Learning, 2014Co-Authors: Masa-aki Takizawa, Masahiro Yukawa, Cedric RichardAbstract:This paper presents a Stochastic Behavior analysis of a kernel-based Stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared error criterion. It also includes stability conditions in the mean and mean-square sense. The present study is based on the analysis of the kernel normalized least mean square (KNLMS) algorithm initially proposed by Chen et al. Simulation results validate the analysis.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm Behavior as a function of these two parameters. This paper studies the steady-state Behavior and the transient Behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.
Jose C. M. Bermudez - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Behavior analysis of the Gaussian KLMS algorithm for a correlated input signal
Signal Processing, 2018Co-Authors: Wemerson D. Parreira, Marcio Costa, Jose C. M. BermudezAbstract:Abstract This paper derives a new model for the Stochastic Behavior of the Gaussian KLMS algorithm. The analysis considers the possibility of time correlated input vectors, a situation that cannot be modeled by existing models. Recursions are derived which predict both the transient and the steady-state Behaviors of the algorithm for a time-varying dictionary. The model predictions show excellent agreement with Monte Carlo simulations in both modes of operation, providing significant improvement when compared to the accuracy of existing models.
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Stochastic Behavior of the nonnegative least mean fourth algorithm for stationary Gaussian inputs and slow learning
Signal Processing, 2016Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to be estimated due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow one to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis. HighlightsThe nonnegative least mean fourth algorithm is proposed for the online estimation.This paper analyzes the Stochastic Behavior of the nonnegative least mean fourth algorithm.The Stochastic Behavior analysis is presented by using new approximations.Simulation results illustrate the accuracy of the proposed analysis.
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Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
arXiv: Numerical Analysis, 2015Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm Behavior as a function of these two parameters. This paper studies the steady-state Behavior and the transient Behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm Behavior as a function of the design parameters. This paper studies the Stochastic Behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm Behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
Jeanyves Tourneret - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm Behavior as a function of these two parameters. This paper studies the steady-state Behavior and the transient Behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm Behavior as a function of the design parameters. This paper studies the Stochastic Behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm Behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
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ICASSP - Stochastic Behavior analysis of the Gaussian Kernel Least Mean Square algorithm
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm Behavior as a function of the design parameters. This paper studies the Stochastic Behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm Behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
Wemerson D. Parreira - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Behavior analysis of the Gaussian KLMS algorithm for a correlated input signal
Signal Processing, 2018Co-Authors: Wemerson D. Parreira, Marcio Costa, Jose C. M. BermudezAbstract:Abstract This paper derives a new model for the Stochastic Behavior of the Gaussian KLMS algorithm. The analysis considers the possibility of time correlated input vectors, a situation that cannot be modeled by existing models. Recursions are derived which predict both the transient and the steady-state Behaviors of the algorithm for a time-varying dictionary. The model predictions show excellent agreement with Monte Carlo simulations in both modes of operation, providing significant improvement when compared to the accuracy of existing models.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
IEEE Transactions on Signal Processing, 2012Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm Behavior as a function of these two parameters. This paper studies the steady-state Behavior and the transient Behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application.
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Stochastic Behavior analysis of the gaussian kernel least mean square algorithm
International Conference on Acoustics Speech and Signal Processing, 2011Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm Behavior as a function of the design parameters. This paper studies the Stochastic Behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm Behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
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ICASSP - Stochastic Behavior analysis of the Gaussian Kernel Least Mean Square algorithm
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: Wemerson D. Parreira, Cedric Richard, Jose C. M. Bermudez, Jeanyves TourneretAbstract:Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm Behavior as a function of the design parameters. This paper studies the Stochastic Behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm Behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
Jie Chen - One of the best experts on this subject based on the ideXlab platform.
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diffusion sign error lms algorithm
Signal Processing, 2016Co-Authors: Jingen Ni, Jie Chen, Xiaoping ChenAbstract:In the case where the measurement noise involves impulsive interference, distributed estimation algorithms based on the mean-square error (MSE) criterion may suffer from severely degraded convergence performance or divergence. To address this problem, we modify the adapt-then-combine (ATC) diffusion LMS (DLMS) algorithm by applying the sign operation to the error signals at all agents to develop a diffusion sign-error LMS (DSE-LMS) algorithm. Furthermore, the Stochastic Behavior of the DSE-LMS algorithm is analyzed for Gaussian inputs and contaminated Gaussian noise based on Price's theorem. Simulation results show the robustness of the DSE-LMS algorithm against impulsive interference and validate the theoretical findings. HighlightsThe diffusion LMS algorithm may suffer from severely degraded convergence performance in impulsive interference environments.The proposed diffusion sign-error LMS algorithm is robust against impulsive interferences.The Stochastic Behavior of the diffusion sign-error LMS algorithm is analyzed based on Price's theorem.
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Stochastic Behavior of the nonnegative least mean fourth algorithm for stationary Gaussian inputs and slow learning
Signal Processing, 2016Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to be estimated due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow one to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis. HighlightsThe nonnegative least mean fourth algorithm is proposed for the online estimation.This paper analyzes the Stochastic Behavior of the nonnegative least mean fourth algorithm.The Stochastic Behavior analysis is presented by using new approximations.Simulation results illustrate the accuracy of the proposed analysis.
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Stochastic Behavior of the Nonnegative Least Mean Fourth Algorithm for Stationary Gaussian Inputs and Slow Learning
arXiv: Numerical Analysis, 2015Co-Authors: Jian Yang, Jie Chen, Cedric Richard, Jose C. M. BermudezAbstract:Some system identification problems impose nonnegativity constraints on the parameters to estimate due to inherent physical characteristics of the unknown system. The nonnegative least-mean-square (NNLMS) algorithm and its variants allow to address this problem in an online manner. A nonnegative least mean fourth (NNLMF) algorithm has been recently proposed to improve the performance of these algorithms in cases where the measurement noise is not Gaussian. This paper provides a first theoretical analysis of the Stochastic Behavior of the NNLMF algorithm for stationary Gaussian inputs and slow learning. Simulation results illustrate the accuracy of the proposed analysis.
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A Modified Non-Negative LMS Algorithm and its Stochastic Behavior Analysis
2011Co-Authors: Jie Chen, Cedric Richard, Jose C. M. Bermudez, Paul HoneineAbstract:In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.
