The Experts below are selected from a list of 96 Experts worldwide ranked by ideXlab platform
Jose C Principe - One of the best experts on this subject based on the ideXlab platform.
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an algorithm based on non Squared Sum of the errors
Signal Processing, 2015Co-Authors: Cristiane Da Silva, Allan Kardec Barros, Ewaldo Santana, Marcos A F De Araujo, Marcus Vinicius Lopes, Joao V Fonseca, Jose C PrincipeAbstract:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted Sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. HighlightsIn this study, we propose a new recursive algorithm that optimizes a Sum of the even powers of the error.In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.This algorithm seems to be faster than usual recursive algorithms presents in the literature.
Cristiane Da Silva - One of the best experts on this subject based on the ideXlab platform.
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an algorithm based on non Squared Sum of the errors
Signal Processing, 2015Co-Authors: Cristiane Da Silva, Allan Kardec Barros, Ewaldo Santana, Marcos A F De Araujo, Marcus Vinicius Lopes, Joao V Fonseca, Jose C PrincipeAbstract:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted Sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. HighlightsIn this study, we propose a new recursive algorithm that optimizes a Sum of the even powers of the error.In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.This algorithm seems to be faster than usual recursive algorithms presents in the literature.
Allan Kardec Barros - One of the best experts on this subject based on the ideXlab platform.
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an algorithm based on non Squared Sum of the errors
Signal Processing, 2015Co-Authors: Cristiane Da Silva, Allan Kardec Barros, Ewaldo Santana, Marcos A F De Araujo, Marcus Vinicius Lopes, Joao V Fonseca, Jose C PrincipeAbstract:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted Sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. HighlightsIn this study, we propose a new recursive algorithm that optimizes a Sum of the even powers of the error.In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.This algorithm seems to be faster than usual recursive algorithms presents in the literature.
Marcos A F De Araujo - One of the best experts on this subject based on the ideXlab platform.
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an algorithm based on non Squared Sum of the errors
Signal Processing, 2015Co-Authors: Cristiane Da Silva, Allan Kardec Barros, Ewaldo Santana, Marcos A F De Araujo, Marcus Vinicius Lopes, Joao V Fonseca, Jose C PrincipeAbstract:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted Sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. HighlightsIn this study, we propose a new recursive algorithm that optimizes a Sum of the even powers of the error.In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.This algorithm seems to be faster than usual recursive algorithms presents in the literature.
Marcus Vinicius Lopes - One of the best experts on this subject based on the ideXlab platform.
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an algorithm based on non Squared Sum of the errors
Signal Processing, 2015Co-Authors: Cristiane Da Silva, Allan Kardec Barros, Ewaldo Santana, Marcos A F De Araujo, Marcus Vinicius Lopes, Joao V Fonseca, Jose C PrincipeAbstract:In adaptive filtering, several algorithms are developed in the quest for greater convergence speed, mostly relying on second order statistics. Here we modify the Recursive Least Square (RLS) equations by using as performance surface a weighted Sum of even error power. As a result, the equations turn out to be simple, elegant, while yielding faster convergence and preserving the computational cost when compared with the existing RLS algorithm. HighlightsIn this study, we propose a new recursive algorithm that optimizes a Sum of the even powers of the error.In adaptive filtering, several algorithms were developed to improve convergence speed, but they rely mostly on second order statistics.This algorithm seems to be faster than usual recursive algorithms presents in the literature.
