A minimum-error entropy criterion with self-adjusting step-size (MEE-SAS)

Han, Seungju; Rao, S.S.; Erdoğmuş, Deni̇z; Jeong, Kyu-Hwa; Prı́ncipe, José C.

doi:10.1016/j.sigpro.2007.05.003

Cited by 30 publications

(10 citation statements)

References 14 publications

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“…This section presents Monte-Carlo simulation results to demonstrate the convergence and steady-state performance of MaxMI criterion in comparison with the widely used MSE criterion and the recently proposed minimum error entropy (MEE) criterion [6][7][8][9][10][11]. For fair comparison, we employ the same identification scheme in which the linear subsystem is identified by stochastic gradient algorithms using, respectively, MaxMI, MSE and MEE criteria, whereas the non-linear subsystem is trained by NLMS algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

Stochastic gradient identification of Wiener system with maximum mutual information criterion

Chen

Zhu

et al. 2011

IET Signal Process.

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This study presents an information-theoretic approach for adaptive identification of an unknown Wiener system. A two-criterion identification scheme is proposed, in which the adaptive system comprises a linear finite-impulse response filter trained by maximum mutual information (MaxMI) criterion and a polynomial non-linearity learned by traditional mean square error criterion. The authors show that under certain conditions, the optimum solution matches the true system exactly. Further, the authors develop a stochastic gradient-based algorithm, that is, stochastic mutual information gradient-normalised least mean square algorithm, to implement the proposed identification scheme. Monte-Carlo simulation results demonstrate the noticeable performance improvement of this new algorithm in comparison with some other algorithms.

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Section: Simulation Resultsmentioning

confidence: 99%

Stochastic gradient identification of Wiener system with maximum mutual information criterion

Chen

Zhu

et al. 2011

IET Signal Process.

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“…Constant stepsize gradient algorithms simply cannot take advantage of such characteristics, so an effort to develop variable stepsize algorithms is in order, or even second order search algorithms. Here we deal with the variable stepsize case [128]. As can be easily inferred from the definition of the information potential Eq.…”

Section: Self-adjusting Stepsize For Mee (Mee-sas)mentioning

confidence: 99%

“…Although the main purpose of this example is to highlight the robustness of correntropy, we also compare performance with the existing ro- bust fitting methods such as least absolute residuals (LAR) (which uses an L1 norm penalty) and bi-square weights (BW) [128]. The parameters of these algorithms are the recommended settings in MATLAB.…”

Section: Linear Regressionmentioning

confidence: 99%

Information Theoretic Learning

Prı́ncipe¹

2010

Information Science and Statistics

643

168

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“…In this paper, we incorporate the minimum error entropy criterion with self-adjusting step-size (MEE-SAS) [8] into the cost function in diffusion distributed estimation. Then we figure out the diffusion-strategy solutions, which are referred to as the diffusion MEE-SAS (DMEE-SAS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

Chen¹,

Ye²,

Shao³

et al. 2017

Preprint

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Abstract:In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

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A minimum-error entropy criterion with self-adjusting step-size (MEE-SAS)

Cited by 30 publications

References 14 publications

Stochastic gradient identification of Wiener system with maximum mutual information criterion

Stochastic gradient identification of Wiener system with maximum mutual information criterion

Information Theoretic Learning

A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

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