Results 261 to 270 of about 72,082 (280)
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Self-similar traffic prediction using least mean kurtosis
Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, 2004Recent studies of high quality; high resolution traffic measurements have revealed that network traffic appears to be statistically self similar. Contrary to the common belief, aggregating self-similar traffic streams can actually intensify rather than diminish burstiness. Thus, traffic prediction plays an important role in network management.
Hong Zhao +2 more
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Stochastic analysis of the Least Mean Kurtosis algorithm for Gaussian inputs
Digital Signal Processing, 2016The Least Mean Kurtosis (LMK) algorithm was initially proposed as an adaptive algorithm that is robust to the observation noise distribution. Good performances of this algorithm have been shown for non-Gaussian additive measurement noise. However, the complexity of the algorithm imposes difficulties for the development of a reasonably complete ...
Neil J. Bershad, José C. M. Bermudez
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The Effect of Skewness and Kurtosis on Mean and Covariance Structure Analysis
Sociological Methods & Research, 2005The maximum likelihood (ML) method, based on the normal distribution assumption, is widely used in mean and covariance structure analysis. With typical nonnormal data, the ML method will lead to biased statistics and inappropriate scientific conclusions.
Ke-Hai Yuan, Peter M. Bentler, Wei Zhang
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Novel quaternion‐valued least‐mean kurtosis adaptive filtering algorithm based on the GHR calculus [PDF]
IET Signal Processing, 2018 A novel quaternion-valued least-mean kurtosis (QLMK) adaptive filtering algorithm is proposed for three- and fourdimensional processes by using the recent generalised Hamilton-real (GHR) calculus.
Engin Cemal Menguc
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Simulation of Twin Data Controlling Population Mean, Variance, Skewness and Kurtosis
Acta geneticae medicae et gemellologiae, 1977A computer system for simulation of quantitative twin data is being developed. The capability is being built in to simulate distributions with known means, standard deviations, skewness and kurtosis.
J C, Christian +5 more
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Multiple least mean kurtosis adaptive filters for blind source separation
Signal, Image and Video Processing, 2020In this paper, a novel use of adaptive filters for blind source separation is presented. The known independent component analysis algorithm separates signals from their mixtures based on the observation that a mixture of statistically independent signals is more Gaussian than the separate signals.
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Signed Least Mean Kurtosis-Based Adaptive Line Enhancer
2006 International Conference on Machine Learning and Cybernetics, 2006Based on the aim of the characteristic of error kurtosis and signed-error, a novel algorithm of sign least mean kurtosis based adaptive line enhancer (SLMKBALE) is proposed. Simulation results have shown that the computational load of the proposed SLMKBALE algorithm is much lower than that of the LMKBALE (least mean kurtosis based adaptive line ...
Ji-cheng Ling, Long-qing He, Ye-cai Guo
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Contourlet Retrieval System Using Absolute Mean and Kurtosis Features
2010 2nd International Conference on Information Engineering and Computer Science, 2010To improve the retrieval rate of contourlet transform retrieval system,a new contourlet retrieval system was proposed.The feature vectors were constructed by cascading the absolute mean energy and kurtosis of each sub-band contourlet coefficients and the similarity measure used here is Canberra distance.
Xin-Wu Chen, Yu-Xi Liu
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Improved Filtered-x Least Mean Kurtosis Algorithm for Active Noise Control
Circuits, Systems, and Signal Processing, 2016The least mean kurtosis (LMK) algorithm has been successfully applied to linear system identification. It outperforms the conventional least mean square method for Gaussian and non-Gaussian noises. The purpose of this work is to apply the LMK algorithm to active noise control (ANC) systems, i.e., to develop a filtered-x LMK (FxLMK) algorithm.
Lu Lu 0005, Haiquan Zhao 0001
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The performance of the fixed-point least mean kurtosis and noisy inputs
48th Midwest Symposium on Circuits and Systems, 2005., 2005Since the optimal solution of the least mean kurtosis (LMK) is selected to minimize the negative of the kurtosis of the error signal, the noise that has symmetrical probability density function (PDF) does not affect the optimal solution. The LMK algorithm has been studied and proved to outperform the widely used LMS algorithm.
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