Suchergebnisse - (( kernel recursive least squares algorithm ) OR ( kernel recursive past squares algorithm ))

Relation: S. Shanmuganathan and S. Samarasinghe, Artificial neural network modelling, vol. 628. Springer, 2016.; Y. Cui, S. Ahmad, and J. Hawkins, “Continuous online sequence learning with an unsupervised neural network model,” Neural computation, vol. 28, no. 11, pp. 2474–2504, 2016.; C. Deb, F. Zhang, J. Yang, S. E. Lee, and K. W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renewable and Sustainable Energy Reviews, vol. 74, pp. 902–924, 2017.; Y. Feng, P. Zhang, M. Yang, Q. Li, and A. Zhang, “Short term load forecasting of offshore oil field microgrids based on da-svm,” Energy Procedia, vol. 158, pp. 2448–2455, 2019.; B. Schölkopf, A. J. Smola, et al., Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2002.; F. Girosi, M. Jones, and T. Poggio, “Regularization theory and neural networks architectures,” Neural computation, vol. 7, no. 2, pp. 219–269, 1995.; B. Schölkopf, A. Smola, and K.-R. Müller, “Nonlinear component analysis as a kernel eigenvalue problem,” Neural computation, vol. 10, no. 5, pp. 1299–1319, 1998.; W. Liu, P. P. Pokharel, and J. C. Principe, “The kernel least-mean-square algorithm,” IEEE Transactions on Signal Processing, vol. 56, no. 2, pp. 543–554, 2008.; Y. Engel, S. Mannor, and R. Meir, “The kernel recursive least-squares algorithm,” IEEE Transactions on signal processing, vol. 52, no. 8, pp. 2275–2285, 2004.; W. Liu, I. Park, Y. Wang, and J. C. Príncipe, “Extended kernel recursive least squares algorithm,” IEEE Transactions on Signal Processing, vol. 57, no. 10, pp. 3801–3814, 2009.; B. Chen, S. Zhao, P. Zhu, and J. C. Príncipe, “Quantized kernel least mean square algorithm,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 1, pp. 22–32, 2012.; H. Fan and Q. Song, “A linear recurrent kernel online learning algorithm with sparse updates,” Neural Networks, vol. 50, pp. 142–153, 2014.; W. Liu, I. Park, and J. C. Principe, “An information theoretic approach of designing sparse kernel adaptive filters,” IEEE Transactions on Neural Networks, vol. 20, no. 12, pp. 1950–1961, 2009.; W. Ao, W.-Q. Xiang, Y.-P. Zhang, L. Wang, C.-Y. Lv, and Z.-H. Wang, “A new variable step size lms adaptive filtering algorithm,” in 2012 International Conference on Computer Science and Electronics Engineering, vol. 2, pp. 265–268, IEEE, 2012.; Q. Niu and T. Chen, “A new variable step size lms adaptive algorithm,” in 2018 Chinese Control And Decision Conference (CCDC), pp. 1–4, IEEE, 2018.; S. Garcia-Vega, X.-J. Zeng, and J. Keane, “Learning from data streams using kernel least-mean-square with multiple kernel-sizes and adaptive step-size,” Neurocomputing, vol. 339, pp. 105–115, 2019.; B. Chen, J. Liang, N. Zheng, and J. C. Príncipe, “Kernel least mean square with adaptive kernel size,” Neurocomputing, vol. 191, pp. 95–106, 2016.; W. W. Qitang Sun, Lujuan Dang and S. Wang, “Kernel least mean square algorithm with mixed kernel,” In Advanced Computational Intelligence (ICACI), 2018 Tenth International Conference, pp. 140–144, 2018.; J. Platt, A resource-allocating network for function interpolation. MIT Press, 1991.; L. Csató and M. Opper, “Sparse on-line gaussian processes,” Neural computation, vol. 14, no. 3, pp. 641–668, 2002.; K. Li and J. C. Principe, “Transfer learning in adaptive filters: The nearest instance centroid-estimation kernel least-mean-square algorithm,” IEEE Transactions on Signal Processing, vol. 65, no. 24, pp. 6520–6535, 2017.; G. Wahba, Spline models for observational data, vol. 59. Siam, 1990.; J. Racine, “An efficient cross-validation algorithm for window width selection for nonparametric kernel regression,” Communications in Statistics-Simulation and Computation, vol. 22, no. 4, pp. 1107–1114, 1993.; E. Herrmann, “Local bandwidth choice in kernel regression estimation,” Journal of Computational and Graphical Statistics, vol. 6, no. 1, pp. 35–54, 1997.; B. W. Silverman, Density estimation for statistics and data analysis. Routledge, 2018.; Y. Gao and S.-L. Xie, “A variable step size lms adaptive filtering algorithm and its analysis,” Acta Electronica Sinica, vol. 29, no. 8, pp. 1094–1097, 2001.; Y. Qian, “A new variable step size algorithm applied in lms adaptive signal processing,” in 2016 Chinese Control and Decision Conference (CCDC), pp. 4326–4329, IEEE, 2016.; UPME, Plan de Expansión de Referencia Generación-Transmisión 2017 - 2031. Unidad de Planeación Minero Energética, 2017.; S. Sagiroglu and D. Sinanc, “Big data: A review,” in 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 42–47, IEEE, 2013.; N. Aronszajn, “Theory of reproducing kernels,” Transactions of the American mathematical society, vol. 68, no. 3, pp. 337–404, 1950.; C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data mining and knowledge discovery, vol. 2, no. 2, pp. 121–167, 1998.; B. Chen, L. Li, W. Liu, and J. C. Príncipe, “Nonlinear adaptive filtering in kernel spaces,” in Springer Handbook of Bio-/Neuroinformatics, pp. 715–734, Springer, 2014.; C. Cheng, A. Sa-Ngasoongsong, O. Beyca, T. Le, H. Yang, Z. Kong, and S. T. Bukkapatnam, “Time series forecasting for nonlinear and non-stationary processes: a review and comparative study,” Iie Transactions, vol. 47, no. 10, pp. 1053–1071, 2015.; B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2001.; K. I. Kim, M. O. Franz, and B. Scholkopf, “Iterative kernel principal component analysis for image modeling,” IEEE transactions on pattern analysis and machine intelligence, vol. 27, no. 9, pp. 1351–1366, 2005.; T.-T. Frieß and R. F. Harrison, “A kernel based adaline.,” in ESANN, vol. 72, pp. 21–23, 1999.; S. An, W. Liu, and S. Venkatesh, “Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression,” Pattern Recognition, vol. 40, no. 8, pp. 2154–2162, 2007.; G. C. Cawley and N. L. Talbot, “Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers,” Pattern Recognition, vol. 36, no. 11, pp. 2585–2592, 2003.; W. Hardle, “ ‘applied nonparametric regression (cambridge: Cambridge university press),” 1990.; C. M. Bishop, Pattern recognition and machine learning. springer, 2006.; R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification. John Wiley & Sons, 2012.; A. K. Jain, “Data clustering: 50 years beyond k-means,” Pattern recognition letters, vol. 31, no. 8, pp. 651–666, 2010.; J. M. Keller, M. R. Gray, and J. A. Givens, “A fuzzy k-nearest neighbor algorithm,” IEEE transactions on systems, man, and cybernetics, no. 4, pp. 580–585, 1985.; K. Fu, Sequential methods in pattern recognition and machine learning, vol. 52. Academic press, 1968.; C. Richard, J. C. M. Bermudez, and P. Honeine, “Online prediction of time series data with kernels,” IEEE Transactions on Signal Processing, vol. 57, no. 3, pp. 1058–1067, 2008.; C. Henry and R. Williams, “Real-time recursive estimation of statistical parameters,” Analytica chimica acta, vol. 242, pp. 17–23, 1991.; C. G. Bezerra, B. S. J. Costa, L. A. Guedes, and P. P. Angelov, “A new evolving clustering algorithm for online data streams,” in 2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), pp. 162–168, IEEE, 2016.; A. G. Salman, Y. Heryadi, E. Abdurahman, and W. Suparta, “Single layer & multilayer long short-term memory (lstm) model with intermediate variables for weather forecasting,” Procedia Computer Science, vol. 135, pp. 89–98, 2018; M. Yukawa and R.-i. Ishii, “On adaptivity of online model selection method based on multikernel adaptive filtering,” in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1–6, IEEE, 2013.; M. Yukawa, “Multikernel adaptive filtering,” IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4672–4682, 2012.; F. A. Tobar, S.-Y. Kung, and D. P. Mandic, “Multikernel least mean square algorithm,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 2, pp. 265– 277, 2013.; T. Ishida and T. Tanaka, “Multikernel adaptive filters with multiple dictionaries and regularization,” in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1–6, IEEE, 2013.; Q. Sun, L. Dang, W. Wang, and S. Wang, “Kernel least mean square algorithm with mixed kernel,” in 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI), pp. 140–144, IEEE, 2018.; T. Burton, D. Sharpe, N. Jenkins, and E. Bossanyi, Wind energy handbook. John Wiley & Sons, 2001.; M. Elshendy, A. F. Colladon, E. Battistoni, and P. A. Gloor, “Using four different online media sources to forecast the crude oil price,” Journal of Information Science, 2017.; P. J. Brockwell and R. A. Davis, Introduction to time series and forecasting. ingerir, 2016.; V. Kotu, “Chapter 12: Time series forecasting, editor (s): Vijay kotu, bala deshpande, data science,” 2019.; Q. Song, X. Zhao, Z. Feng, and B. Song, “Recursive least squares algorithm with adaptive forgetting factor based on echo state network,” in 2011 9th World Congress on Intelligent Control and Automation, pp. 295–298, IEEE, 2011.; S. Wen, R. Hu, Y. Yang, T. Huang, Z. Zeng, and Y.-D. Song, “Memristor-based echo state network with online least mean square,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, no. 99, pp. 1–10, 2018.; W.-Y. Chang, “A literature review of wind forecasting methods,” Journal of Power and Energy Engineering, vol. 2, no. 4, 2014.; C. Voyant, G. Notton, S. Kalogirou, M.-L. Nivet, C. Paoli, F. Motte, and A. Fouilloy, “Machine learning methods for solar radiation forecasting: A review,” Renewable Energy, vol. 105, pp. 569–582, 2017.; G. Bontempi, S. B. Taieb, and Y.-A. Le Borgne, “Machine learning strategies for time series forecasting,” in European business intelligence summer school, pp. 62–77, Springer, 2012.; L. Yu, Y. Zhao, L. Tang, and Z. Yang, “Online big data-driven oil consumption forecasting with google trends,” International Journal of Forecasting, vol. 35, no. 1, pp. 213– 223, 2019.; O. Schaer, N. Kourentzes, and R. Fildes, “Demand forecasting with user-generated online; A. S.Weigend, Time series prediction: forecasting the future and understanding the past. Routledge, 2018.; F. Kaytez, M. C. Taplamacioglu, E. Cam, and F. Hardalac, “Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines,” International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431–438, 2015.; W. Liu, J. C. Principe, and S. Haykin, Kernel adaptive filtering: a comprehensive introduction, vol. 57. John Wiley & Sons, 2011.; https://repositorio.unal.edu.co/handle/unal/75985