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Silviu Ciochina - One of the best experts on this subject based on the ideXlab platform.
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A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019Co-Authors: Laura Maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the Systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the System Identification Problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse bilinear forms, in which the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the Identification of such bilinear forms. Experimental results highlight the good behavior of the proposed solution.
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a recursive least squares algorithm based on the nearest kronecker product decomposition
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Camelia Eliseiiliescu, Jacob Benesty, Constantin Paleologu, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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ICASSP - A Recursive Least-squares Algorithm Based on the Nearest Kronecker Product Decomposition
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
2018 International Symposium on Electronics and Telecommunications (ISETC), 2018Co-Authors: Camelia Elisei-iliescu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Constantin Paleologu, Jacob BenestyAbstract:The System Identification Problem is more challenging when the parameter space becomes large. This paper addresses the Identification of bilinear Systems based on the regularized recursive least-squares algorithm. Here, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.
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TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Cristian Stanciu, Cristian Anghel, Jacob Benesty, Silviu CiochinaAbstract:Proportionate-type algorithms are designed to exploit the sparseness character of the Systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the System Identification Problem becomes more challenging. In this paper, we focus on the Identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the Identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
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A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019Co-Authors: Laura Maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the Systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the System Identification Problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse bilinear forms, in which the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the Identification of such bilinear forms. Experimental results highlight the good behavior of the proposed solution.
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a recursive least squares algorithm based on the nearest kronecker product decomposition
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Camelia Eliseiiliescu, Jacob Benesty, Constantin Paleologu, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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ICASSP - A Recursive Least-squares Algorithm Based on the Nearest Kronecker Product Decomposition
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
2018 International Symposium on Electronics and Telecommunications (ISETC), 2018Co-Authors: Camelia Elisei-iliescu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Constantin Paleologu, Jacob BenestyAbstract:The System Identification Problem is more challenging when the parameter space becomes large. This paper addresses the Identification of bilinear Systems based on the regularized recursive least-squares algorithm. Here, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.
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TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Cristian Stanciu, Cristian Anghel, Jacob Benesty, Silviu CiochinaAbstract:Proportionate-type algorithms are designed to exploit the sparseness character of the Systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the System Identification Problem becomes more challenging. In this paper, we focus on the Identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the Identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
Constantin Paleologu - One of the best experts on this subject based on the ideXlab platform.
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A Proportionate Affine Projection Algorithm for the Identification of Sparse Bilinear Forms
2019 International Symposium on Signals Circuits and Systems (ISSCS), 2019Co-Authors: Laura Maria Dogariu, Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:Identification of sparse impulse responses was addressed mainly in the last two decades with the development of the so-called “proportionate”-type algorithms. These algorithms are meant to exploit the sparseness of the Systems that need to be identified, with the purpose of improving the convergence rate and tracking of the conventional adaptive algorithms used in this framework. Nevertheless, the System Identification Problem becomes more challenging when the parameter space is large. This issue can be addressed with tensor decompositions and modelling. In this paper, we aim to identify sparse bilinear forms, in which the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this context, we derive a proportionate affine projection algorithm for the Identification of such bilinear forms. Experimental results highlight the good behavior of the proposed solution.
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a recursive least squares algorithm based on the nearest kronecker product decomposition
International Conference on Acoustics Speech and Signal Processing, 2019Co-Authors: Camelia Eliseiiliescu, Jacob Benesty, Constantin Paleologu, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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ICASSP - A Recursive Least-squares Algorithm Based on the Nearest Kronecker Product Decomposition
ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019Co-Authors: Camelia Elisei-iliescu, Constantin Paleologu, Jacob Benesty, Silviu CiochinaAbstract:The recursive least-squares (RLS) adaptive filter is an appealing choice in System Identification Problems, mainly due to its fast convergence rate. However, this algorithm is computationally very complex, which may make it useless for the Identification of high length impulse responses, like in echo cancellation. In this paper, we focus on a new approach to improve the efficiency of the RLS algorithm. The basic idea is to exploit the impulse response decomposition based on the nearest Kronecker product and low-rank approximation. Thus, a high-dimension System Identification Problem is reformulated in terms of low-dimension Problems, which are tensorized together. Simulations performed in the context of echo cancellation indicate the good performance of the RLS algorithm based on this approach.
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Regularized Recursive Least-Squares Algorithms for the Identification of Bilinear Forms
2018 International Symposium on Electronics and Telecommunications (ISETC), 2018Co-Authors: Camelia Elisei-iliescu, Silviu Ciochina, Cristian Stanciu, Cristian Anghel, Constantin Paleologu, Jacob BenestyAbstract:The System Identification Problem is more challenging when the parameter space becomes large. This paper addresses the Identification of bilinear Systems based on the regularized recursive least-squares algorithm. Here, the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In order to improve the robustness of the algorithm in noisy environments, a variable-regularized version is also developed, where the regularization parameters are adjusted using an estimation of the signal-to-noise ratio. Simulation results outline the appealing features of these algorithms.
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TSP - A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms
2018 41st International Conference on Telecommunications and Signal Processing (TSP), 2018Co-Authors: Constantin Paleologu, Camelia Elisei-iliescu, Cristian Stanciu, Cristian Anghel, Jacob Benesty, Silviu CiochinaAbstract:Proportionate-type algorithms are designed to exploit the sparseness character of the Systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the System Identification Problem becomes more challenging. In this paper, we focus on the Identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the Identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
Wang Jun - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear System Identification using Scaling Kernel Support Vector Regression
Computer Simulation, 2006Co-Authors: Wang JunAbstract:A new scaling kernel support vector regression was proposed for nonlinear System Identification Problem . Using linear programming technique and scaling kernel function, the support vector regression model was obtained. The kernel function of support vector regression doesn’t need to meet Mercer condition so as to offer more flexibility for selecting support kernel in practice application. The simulation results show that the scaling kernel support vector regression method can become the powerful tool for the nonlinear System Identification.
Zhang Yuan - One of the best experts on this subject based on the ideXlab platform.
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APPLICATION OF SUPPORT VECTOR REGRESSION TO NONLINEAR System Identification
Information & Computation, 2003Co-Authors: Zhang YuanAbstract:This paper applies Support Vector Regression (SVR) to nonlinear System Identification Problem. Using the basic idea of Gaussian SVR and e insensitive loss function, we propose a new algorithm for nonlinear System Identification and compare the Gaussian SVR with the radial basis function (RBF) network for System Identification. The performance of the SVR is illustrated by a simulation example involving a benchmark nonlinear System.
