Relevant bibliographies by topics / Kernel Least Mean Square (KLMS)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Kernel Least Mean Square (KLMS)'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Kernel Least Mean Square (KLMS)"

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Chaudhary, Naveed Ishtiaq, Muhammad Asif Zahoor Raja, Junaid Ali Khan, and Muhammad Saeed Aslam. "Identification of Input Nonlinear Control Autoregressive Systems Using Fractional Signal Processing Approach." Scientific World Journal 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/467276.

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A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that
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Wu, Qishuai, Yingsong Li, and Wei Xue. "A Kernel Recursive Maximum Versoria-Like Criterion Algorithm for Nonlinear Channel Equalization." Symmetry 11, no. 9 (2019): 1067. http://dx.doi.org/10.3390/sym11091067.

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In this paper, a kernel recursive maximum Versoria-like criterion (KRMVLC) algorithm has been constructed, derived, and analyzed within the framework of nonlinear adaptive filtering (AF), which considers the benefits of logarithmic second-order errors and the symmetry maximum-Versoria criterion (MVC) lying in reproducing the kernel Hilbert space (RKHS). In the devised KRMVLC, the Versoria approach aims to resist the impulse noise. The proposed KRMVLC algorithm was carefully derived for taking the nonlinear channel equalization (NCE) under different non-Gaussian interferences. The achieved resu
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Chen, Badong, Junli Liang, Nanning Zheng, and José C. Príncipe. "Kernel least mean square with adaptive kernel size." Neurocomputing 191 (May 2016): 95–106. http://dx.doi.org/10.1016/j.neucom.2016.01.004.

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Liu, Weifeng, Puskal P. Pokharel, and Jose C. Principe. "The Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 56, no. 2 (2008): 543–54. http://dx.doi.org/10.1109/tsp.2007.907881.

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Badong Chen, Songlin Zhao, Pingping Zhu, and J. C. Principe. "Quantized Kernel Least Mean Square Algorithm." IEEE Transactions on Neural Networks and Learning Systems 23, no. 1 (2012): 22–32. http://dx.doi.org/10.1109/tnnls.2011.2178446.

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Chen, Badong, Songlin Zhao, Pingping Zhu, and José C. Príncipe. "Mean square convergence analysis for kernel least mean square algorithm." Signal Processing 92, no. 11 (2012): 2624–32. http://dx.doi.org/10.1016/j.sigpro.2012.04.007.

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MURUGAN, S. SAKTHIVEL, V. NATARAJAN, and S. RADHA. "ANALYSIS OF MNLMS AND KLMS ALGORITHM FOR UNDERWATER ACOUSTIC COMMUNICATIONS." Fluctuation and Noise Letters 11, no. 04 (2012): 1250023. http://dx.doi.org/10.1142/s021947751250023x.

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The use of adaptive filters to alleviate the degradation caused by wind driven ambient noise in shallow water is considered in this paper. Since, underwater acoustic signals are greatly affected by the ocean interference and ambient noise disturbances when propagating through underwater channels, an effective adaptive filtering system is necessary for denoising the signal which are degraded by noise. Least mean square (LMS), normalized LMS (NLMS), Modified New LMS (MNLMS) and Kalman LMS (KLMS) based adaptive algorithms are analyzed in terms of their performance with the aid of performance meas
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Zhao, Ji, Xiaofeng Liao, Shiyuan Wang, and Chi K. Tse. "Kernel Least Mean Square with Single Feedback." IEEE Signal Processing Letters 22, no. 7 (2015): 953–57. http://dx.doi.org/10.1109/lsp.2014.2377726.

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Boloix-Tortosa, Rafael, Juan Jose Murillo-Fuentes, and Sotirios A. Tsaftaris. "The Generalized Complex Kernel Least-Mean-Square Algorithm." IEEE Transactions on Signal Processing 67, no. 20 (2019): 5213–22. http://dx.doi.org/10.1109/tsp.2019.2937289.

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Pokharel, Puskal P., Weifeng Liu, and Jose C. Principe. "Kernel least mean square algorithm with constrained growth." Signal Processing 89, no. 3 (2009): 257–65. http://dx.doi.org/10.1016/j.sigpro.2008.08.009.

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Dissertations / Theses on the topic "Kernel Least Mean Square (KLMS)"

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Silva, Éden Pereira da. "Proposta do Kernel Sigmoide (KSIG) e sua análise de convergência para a solução de problemas de filtragem adaptativa não linear." Universidade Federal de Sergipe, 2017. https://ri.ufs.br/handle/riufs/3393.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES<br>Adaptive filtering is applied as solution for many problems in engineer. There are many techniques to improve adaptive filtering as kernel methods and, in addiction, it is used a pretuned dictionary. In this context, here is presented the KSIG algorithm, the kernel version of Sigmoide, where is used the kernel, to decrease the error, and the non-linear and even cost function to increase the convergence speed. Here it is described also, the KSIG with a pretuned dictionary, to reduce the size of the data set used to calc
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Parreira, Wemerson Delcio. "Comportamento estocástico do algoritmo kernel least-mean-square." Florianópolis, 2012. http://repositorio.ufsc.br/xmlui/handle/123456789/99402.

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Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia Elétrica.<br>Made available in DSpace on 2013-03-04T20:02:58Z (GMT). No. of bitstreams: 1 307884.pdf: 2324151 bytes, checksum: e8836a0a1ca734d1939b5144cef51992 (MD5)<br>Algoritmos baseados em kernel têm-se tornado populares no processamento não-linear de sinais. O processamento não-linear aplicado sobre um sinal pode ser modelado como um processamento linear aplicado a um sinal transformado para um espaço de Hilbert com kernels reprodutivos (RKHS). A operação linear no espaço
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Gao, Wei. "Kernel LMS à noyau gaussien : conception, analyse et applications à divers contextes." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4076/document.

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L’objectif principal de cette thèse est de décliner et d’analyser l’algorithme kernel-LMS à noyau Gaussien dans trois cadres différents: celui des noyaux uniques et multiples, à valeurs réelles et à valeurs complexes, dans un contexte d’apprentissage distributé et coopératif dans les réseaux de capteurs. Plus précisement, ce travail s’intéresse à l’analyse du comportement en moyenne et en erreur quadratique de cas différents types d’algorithmes LMS à noyau. Les modèles analytiques de convergence obtenus sont validés par des simulations numérique. Tout d’abord, nous introduiso
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Book chapters on the topic "Kernel Least Mean Square (KLMS)"

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Ji, Hong, Badong Chen, Zejian Yuan, Nanning Zheng, Andreas Keil, and Jose C. Príncipe. "Online Nonlinear Granger Causality Detection by Quantized Kernel Least Mean Square." In Neural Information Processing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12640-1_9.

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Ahmad, Noor A., and Shazia Javed. "Chaotic Time Series Prediction Using Random Fourier Feature Kernel Least Mean Square Algorithm with Adaptive Kernel Size." In Springer Proceedings in Mathematics & Statistics. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2629-6_17.

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Conference papers on the topic "Kernel Least Mean Square (KLMS)"

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De Luca, Patrick Medeiros, and Wemerson Delcio Parreira. "Simulação do comportamento estocástico do algoritmo KLMS com diferentes kernels." In Computer on the Beach. Universidade do Vale do Itajaí, 2020. http://dx.doi.org/10.14210/cotb.v11n1.p004-006.

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The kernel least-mean-square (KLMS) algorithm is a popular algorithmin nonlinear adaptive filtering due to its simplicity androbustness. In kernel adaptive filtering, the statistics of the inputto the linear filter depends on the kernel and its parameters. Moreover,practical implementations on systems estimation require afinite non-linearity model order. In order to obtain finite ordermodels, many kernelized adaptive filters use a dictionary of kernelfunctions. Dictionary size also depends on the kernel and itsparameters. Therefore, KLMS may have different performanceson the estimation of a no
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Lin Li, Il Memming Park, Sohan Seth, et al. "An adaptive decoder from spike trains to micro-stimulation using kernel least-mean-squares (KLMS)." In 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2011. http://dx.doi.org/10.1109/mlsp.2011.6064603.

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Pokharel, Rosha, Sohan Seth, and Jose C. Principe. "Mixture kernel least mean square." In 2013 International Joint Conference on Neural Networks (IJCNN 2013 - Dallas). IEEE, 2013. http://dx.doi.org/10.1109/ijcnn.2013.6706867.

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Pokharel, Rosha, Sohan Seth, and Jose C. Principe. "Quantized mixture kernel least mean square." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889975.

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Wang, Wanli, Shiyuan Wang, Guobing Qian, and Bo Yang. "Kernel least mean square with tracking." In 2017 36th Chinese Control Conference (CCC). IEEE, 2017. http://dx.doi.org/10.23919/chicc.2017.8028160.

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Sun, Qitang, Lujuan Dang, Wanli Wang, and Shiyuan Wang. "Kernel least mean square algorithm with mixed kernel." In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377595.

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Xi, Bao, Lei Sun, Badong Chen, Jianji Wang, Nanning Zheng, and Jose C. Principe. "Density-dependent quantized kernel least mean square." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727657.

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Zhao, Zhijin, and Mingming Jin. "The decorrelated kernel least-mean-square algorithm." In 2016 IEEE 13th International Conference on Signal Processing (ICSP). IEEE, 2016. http://dx.doi.org/10.1109/icsp.2016.7877857.

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Peng, Siyuan, Zongze Wu, Wentao Ma, and Badong Chen. "Kernel least mean square based on conjugate gradient." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952666.

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Chen, Badong, Zhengda Qin, and Lei Sun. "Steady-state mean square performance of a sparsified kernel least mean square algorithm." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7952647.

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Journal articles
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