Výsledky vyhledávání - adaptive forgetting factor recursive past square algorithm

Data-Driven Adaptive Steady-State-Integral-Derivative Controller Using Recursive Least Squares With Performance Conditions

Autoři: Jeongwoo Lee Kwangseok Oh

Zdroj: IEEE Access, Vol 11, Pp 54616-54628 (2023)

Témata: Data-driven adaptive control, steady state-integral-derivative control, gradient descent, recursive least squares, forgetting factor, performance condition, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Popis souboru: electronic resource

Relation: https://ieeexplore.ieee.org/document/10138583/; https://doaj.org/toc/2169-3536

Přístupová URL adresa: https://doaj.org/article/df4272deef324be7bfa2e82f9bfaded1

Variable Recursive Least Square Algorithm for Lithium-ion Battery Equivalent Circuit Model Parameters Identification.

Autoři: El Marghichi, Mouncef Loulijat, Azedine El Hantati, Issam

Zdroj: Periodica Polytechnica: Electrical Engineering & Computer Science; 2023, Vol. 67 Issue 3, p239-248, 10p

Témata: PARAMETER identification, LEAST squares, LITHIUM-ion batteries, ALGORITHMS, ELECTRIC circuits, PROBLEM solving, SENSOR networks

Korporace: SAMSUNG Group (Company)

Variable recursive least square algorithm for online battery equivalent circuit model parameters identification for electric vehicles.

Autoři: El Marghichi, Mouncef Loulijat, Azeddine El Hantati, Issam

Zdroj: Electrical Engineering; Jun2024, Vol. 106 Issue 3, p2425-2445, 21p

Témata: ONLINE algorithms, PARAMETER identification, LEAST squares, ELECTRIC circuits, LITHIUM cells

Korporace: SAMSUNG Group (Company)

Improved-Variable-Forgetting-Factor Recursive Algorithm Based on the Logarithmic Cost for Volterra System Identification.

Autoři: Lu Zhao, Haiquan Chen, Badong

Zdroj: IEEE Transactions on Circuits & Systems. Part II: Express Briefs; Jun2016, Vol. 63 Issue 6, p588-592, 5p

Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm.

Autoři: Xia, Bizhong Huang, Rui Lao, Zizhou a další

Zdroj: Energies (19961073); Nov2018, Vol. 11 Issue 11, p3180, 1p

Témata: LEAST squares, KALMAN filtering, LITHIUM-ion batteries, PARTICLE swarm optimization, TEMPERATURE

Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators.

Autoři: Xu, Xiaohui Deng, Ke Yang, Jibin a další

Zdroj: Sustainability (2071-1050); Feb2025, Vol. 17 Issue 3, p812, 24p

Adaptive Model Predictive Control Research on Regenerative Braking for Electric Bus Cruising Downhill.

Autoři: Guo-Zhu, Zhao Xiang, Huang Xing, Peng

Zdroj: Journal of Advanced Manufacturing Systems; Sep2016, Vol. 15 Issue 3, p133-150, 18p, 1 Diagram, 1 Chart, 2 Graphs

Témata: ADAPTIVE control systems, PREDICTIVE control systems, REGENERATIVE braking, BUSES, LEAST squares, ELECTRIC motors, BRAKE systems

Development of Multivariable Dynamic System Response Curve Method for Real‐Time Flood Forecasting Correction.

Autoři: Sun, Y. Bao, W. Jiang, P. a další

Zdroj: Water Resources Research; Jul2018, Vol. 54 Issue 7, p4730-4749, 20p

Témata: HYDROLOGIC models, AUTOREGRESSION (Statistics)

Adaptive Practical Nonlinear Model Predictive Control for Echo State Network Models.

Autoři: Schwedersky, Bernardo Barancelli Flesch, Rodolfo Cesar Costa Rovea, Samuel Bahu

Zdroj: IEEE Transactions on Neural Networks & Learning Systems; Jun2022, Vol. 33 Issue 6, p2605-2614, 10p

Témata: PREDICTIVE control systems, PREDICTION models, DYNAMICAL systems, NONLINEAR systems, ADAPTIVE control systems

Kernel Recursive Least-Squares Tracker for Time-Varying Regression.

Autoři: Van Vaerenbergh, Steven Lazaro-Gredilla, Miguel Santamaria, Ignacio

Zdroj: IEEE Transactions on Neural Networks & Learning Systems; Aug2012, Vol. 23 Issue 8, p1313-1326, 14p

Témata: SIGNAL processing, KERNEL (Mathematics), ADAPTIVE filters, COMPUTATIONAL complexity, COMPUTER simulation

Cascaded RLS-LMS Prediction in MPEG-4 Lossless Audio Coding.

Autoři: Huang, Haibin Fränti, Pasi Dongyan Huang a další

Zdroj: IEEE Transactions on Audio, Speech & Language Processing; Mar2008, Vol. 16 Issue 3, p554-562, 9p, 3 Diagrams, 5 Charts, 5 Graphs

Témata: MPEG (Video coding standard), DATA compression, CODING theory, ADAPTIVE filters, LEAST squares, DIGITAL audio

An H∞ Optimization and Its Fast Algorithm for Time-Variant System Identification.

Autoři: Nishiyama, Kiyoshi

Zdroj: IEEE Transactions on Signal Processing; May2004, Vol. 52 Issue 5, p1335-1342, 8p

Témata: RICCATI equation, ESTIMATION theory, DIGITAL signal processing, ALGORITHMS, ELECTROMAGNETIC noise, KALMAN filtering

Efficient Online Learning Algorithms for Total Least Square Problems

Autoři: Xiangyu Kong Dazheng Feng

Resource Type: eBook.

Témata: Mathematical models, Control engineering, Algorithms, Mathematics—Data processing

Categories: MATHEMATICS / Applied, COMPUTERS / Computer Science, COMPUTERS / Programming / Algorithms, TECHNOLOGY & ENGINEERING / Electrical

A framework for online prediction using kernel adaptive filtering ; Marco de predicción en línea usando filtros adaptativos kernel

Autoři: León Gómez, Eder Arley Castellanos Domínguez, César Germán Garcia Vega, Sergio a další

Přispěvatelé: León Gómez, Eder Arley Castellanos Domínguez, César Germán Garcia Vega, Sergio a další

Témata: 620 - Ingeniería y operaciones afines, Machine learning, Forecasts, Kernel adaptative filtering, Dictionary, Learning rate, Kernel bandwidth, Clustering adaptive, Aprendizaje de máquina, Predicción, Filtros adaptativos Kernel, Diccionario, Tasa de aprendizaje, Ancho de banda del Kernel, Agrupamiento adaptativo

Popis souboru: application/pdf

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

Dostupnost: https://repositorio.unal.edu.co/handle/unal/75985

Consistency Evaluation of Electric Vehicle Battery Pack: Multi-feature Information Fusion Approach

Autoři: Jiaqiang Tian Guoyi Chang Xinghua Liu a další

Přispěvatelé: Jiaqiang Tian Guoyi Chang Xinghua Liu a další

Zdroj: IEEE Transactions on Vehicular Technology. :1-12

Témata: Consistency Evaluation, Electrical and electronic engineering [Engineering], 7. Clean energy, Energy Storage Systems

Přístupová URL adresa: https://hdl.handle.net/10356/171783

Weighted Multimodel Predictive Function Control for Automatic Train Operation System.

Autoři: Shuhuan Wen Jingwei Yang Rad, Ahmad B. a další

Zdroj: Journal of Applied Mathematics; 2014, p1-8, 8p

Témata: AUTOMATIC train control, PREDICTIVE control systems, FUZZY control systems, K-means clustering, PREDICTION models, COMPUTER simulation, NONLINEAR theories

Novel Lossless Compression Method for Hyperspectral Images Based on Variable Forgetting Factor Recursive Least Squares.

Autoři: Changguo Li Fuquan Zhu

Zdroj: Journal of Information Processing Systems; Oct2024, Vol. 20 Issue 5, p663-674, 12p

Research on the On-Line Identification of Ship Maneuvering Motion Model Parameters and Adaptive Control.

Autoři: Liu, Jinlai Chang, Lubin Xu, Luping a další

Zdroj: Journal of Marine Science & Engineering; Apr2025, Vol. 13 Issue 4, p753, 22p

Témata: ADAPTIVE control systems, INTELLIGENT control systems, SHIP models, ALGORITHMS, SHIPS

Identifying the parameters of ultracapacitors based on variable forgetting factor recursive least square.

Autoři: Zhang, Bo Chen, Xi You, Hanwen a další

Zdroj: COMPEL; 2024, Vol. 43 Issue 6, p1220-1238, 19p

Témata: STANDARD deviations, SUPERCAPACITORS, ENERGY density, PARAMETER identification, SURFACE charges

Research on a High-Precision State-of-Charge Estimation Method Based on Forgetting Factor Recursive Least Squares and Adaptive Extended Kalman Filter Applied to LiFePO4 Battery.

Autoři: Xia, Yihui Ye, Zhihao Huang, Liming a další

Zdroj: Electronics (2079-9292); Sep2023, Vol. 12 Issue 17, p3670, 15p

Témata: KALMAN filtering, PARAMETER identification, STORAGE batteries