Adaptive Blind Deconvolution of Linear Channels Using Renyi's Entropy with Parzen Window Estimation

Erdoğmuş, Deni̇z; Hild, Kenneth E.; Prı́ncipe, José C.; Lázaro, Marcelino; Santamarı́a, Ignacio

doi:10.1109/tsp.2004.827202

Cited by 48 publications

(26 citation statements)

References 30 publications

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“…Besides that, for linear equalizers and under the hypothesis of Gaussianity, the blind ITL criteria behave similarly to the CM, but, for impulsive noise models, the latter loses performance. Similar ITL blind methods for deconvolution can be found in [37], [38] and [39].…”

Section: ) Supervised Equalizationmentioning

confidence: 98%

An Introduction to Information Theoretic Learning, Part II: Applications

Silva

Fantinato

Canuto

et al. 2016

JCIS

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Abstract-This is the second part of the introductory tutorial about information theoretic learning, which, after the theoretical foundations presented in Part I, now discusses the concepts of correntropy, a new similarity measure derived from the quadratic entropy, and presents example problems where the ITL framework can be successfully applied: dynamic modelling, equalization, independent component analysis and cluster analysis.

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Section: ) Supervised Equalizationmentioning

confidence: 98%

An Introduction to Information Theoretic Learning, Part II: Applications

Silva

Fantinato

Canuto

et al. 2016

JCIS

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“…According to Figure 6, when 0 α > and 0 α ≠ , with the increase of α, the statistical range of Renyi entropy will expand for the system state of small probability event, and the statistical sensitivity for the small probability event will reduce correspondingly. In contrast, with the decrease of α, the statistical range of small probability event is reduced, and the statistical sensitivity increases [26]. For a signal containing transient components, the components of the signal are considered as the small probability event.…”

Section: The Statistical Properties Of Renyi Entropymentioning

confidence: 99%

A Transient Fault Recognition Method for an AC-DC Hybrid Transmission System Based on MMC Information Fusion

Chen

Dou

et al. 2016

Energies

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At present, the research is still in the primary stage in the process of fault disturbance energy transfer in the multilevel modular converter based high voltage direct current (HVDC-MMC). An urgent problem is how to extract and analyze the fault features hidden in MMC electrical information in further studies on the HVDC system. Aiming at the above, this article analyzes the influence of AC transient disturbance on electrical signals of MMC. At the same time, it is found that the energy distribution of electrical signals in MMC is different for different arms in the same frequency bands after the discrete wavelet packet transformation (DWPT). Renyi wavelet packet energy entropy (RWPEE) and Renyi wavelet packet time entropy (RWPTE) are proposed and applied to AC transient fault feature extraction from electrical signals in MMC. Using the feature extraction results of Renyi wavelet packet entropy (RWPE), a novel recognition method is put forward to recognize AC transient faults using the information fusion technology. Theoretical analysis and experimental results show that the proposed method is available to recognize transient AC faults.

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“…In this work, the conditional H HM P for each state i was estimated using two different non-parametric entropy estimators (Shannon and Renyi's entropies, respectively) given by [33]:…”

Section: Estimation Of the Hmm-based Entropiesmentioning

confidence: 99%

Entropies from Markov Models as Complexity Measures of Embedded Attractors

Arias-Londoño

Godino-Llorente

2015

Entropy

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This paper addresses the problem of measuring complexity from embedded attractors as a way to characterize changes in the dynamical behavior of different types of systems with a quasi-periodic behavior by observing their outputs. With the aim of measuring the stability of the trajectories of the attractor along time, this paper proposes three new estimations of entropy that are derived from a Markov model of the embedded attractor. The proposed estimators are compared with traditional nonparametric entropy measures, such as approximate entropy, sample entropy and fuzzy entropy, which only take into account the spatial dimension of the trajectory. The method proposes the use of an unsupervised algorithm to find the principal curve, which is considered as the "profile trajectory", that will serve to adjust the Markov model. The new entropy measures are evaluated using three synthetic experiments and three datasets of physiological signals. In terms of consistency and discrimination capabilities, the results show that the proposed measures perform better than the other entropy measures used for comparison purposes.

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Adaptive Blind Deconvolution of Linear Channels Using Renyi's Entropy with Parzen Window Estimation

Cited by 48 publications

References 30 publications

An Introduction to Information Theoretic Learning, Part II: Applications

An Introduction to Information Theoretic Learning, Part II: Applications

A Transient Fault Recognition Method for an AC-DC Hybrid Transmission System Based on MMC Information Fusion

Entropies from Markov Models as Complexity Measures of Embedded Attractors

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