Výsledky vyhledávání - "Stochastic Gradient Algorithm"

Distributed Extended Stochastic Gradient Algorithm for Joint Identification of System Parameters and Noise Model Parameters: Distributed extended stochastic gradient algorithm for joint identification of system parameters and noise model parameters

Autoři: Die Gan Ren Yan Shuning Chen a další

Zdroj: SIAM Journal on Control and Optimization. 63:650-675

Témata: distributed identification, ARMAX model, colored noise, Randomized algorithms, Networked control, stochastic gradient algorithm, Stochastic systems in control theory (general), almost sure convergence, System identification, sensor network

Popis souboru: application/xml

Parameter estimation of fractional‐order Hammerstein state space system based on the extended Kalman filter: Parameter estimation of fractional-order Hammerstein state space system based on the extended Kalman filter

Autoři: Yiqun Bi Yan Ji

Zdroj: International Journal of Adaptive Control and Signal Processing. 37:1827-1846

Témata: extended Kalman filtering, forgetting factor, 0209 industrial biotechnology, Estimation and detection in stochastic control theory, hierarchical identification, Identification in stochastic control theory, Least squares and related methods for stochastic control systems, 0202 electrical engineering, electronic engineering, information engineering, stochastic gradient algorithm, Nonlinear systems in control theory, 02 engineering and technology, fractional-order system, Filtering in stochastic control theory

Popis souboru: application/xml

Estimation and variable selection in high dimension in nonlinear mixed-effects models.

Autoři: Caillebotte, Antoine Kuhn, Estelle Lemler, Sarah a další

Přispěvatelé: Caillebotte, Antoine Kuhn, Estelle Lemler, Sarah a další

Zdroj: https://hal.inrae.fr/hal-05005304 ; 2025.

Témata: Non-linear mixed effect models, Proximal operator, Stochastic gradient algorithm, High dimension, Variable selection, nonlinear mixed effects model high dimension variable selection LASSO penalty stochastic gradient descent weighted proximal, nonlinear mixed effects model, LASSO penalty, stochastic gradient descent, weighted proximal, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]

Dostupnost: https://hal.inrae.fr/hal-05005304
https://hal.inrae.fr/hal-05005304v3/document
https://hal.inrae.fr/hal-05005304v3/file/preprint_NLME_HD_Caillebotteetal_20250731.pdf

G+E copula model to improve the estimation of the genetic parameters in bivariate mixed model

Autoři: Rohmer, Tom Bruning, Victoria Kuhn, Estelle a další

Přispěvatelé: Rohmer, Tom Bruning, Victoria Kuhn, Estelle a další

Zdroj: https://hal.inrae.fr/hal-04995008 ; 2025.

Témata: Quantitative genetics, Linear mixed model, Copula, Stochastic gradient algorithm, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]

Dostupnost: https://hal.inrae.fr/hal-04995008
https://hal.inrae.fr/hal-04995008v1/document
https://hal.inrae.fr/hal-04995008v1/file/CopGenMod-HAL.pdf

Multi-innovation gradient estimation algorithms and convergence analysis for feedback nonlinear equation-error moving average systems

Autoři: Wei, Chun Liu, Yanjun Ding, Feng

Zdroj: Journal of the Franklin Institute. 359:10145-10171

Témata: 0209 industrial biotechnology, Estimation and detection in stochastic control theory, feedback nonlinear equation-error systems, extended stochastic gradient algorithm, 0202 electrical engineering, electronic engineering, information engineering, Nonlinear systems in control theory, 02 engineering and technology, Feedback control, parameter estimation

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/7617730
https://doi.org/10.1016/j.jfranklin.2022.08.045

On Large Batch Training and Sharp Minima: A Fokker–Planck Perspective

Autoři: Dai, Xiaowu Zhu, Yuhua

Zdroj: Journal of Statistical Theory and Practice. 14(3)

Témata: Mathematical Sciences, Statistics, Large batch training, Sharp minima, Fokker-Planck equation, Stochastic gradient algorithm, Deep neural network

Popis souboru: application/pdf

Přístupová URL adresa: https://escholarship.org/uc/item/8gf7x1s7
https://escholarship.org/content/qt8gf7x1s7/qt8gf7x1s7.pdf

Identification of a nonlinear rational model based on bias compensated multi-innovation stochastic gradient algorithm

Autoři: S. X. Jing R. R. Liu L. Chen

Zdroj: Automatika, Vol 63, Iss 4, Pp 785-792 (2022)
Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije
Volume 63
Issue 4

Témata: T59.5, Automation, 0209 industrial biotechnology, Control engineering systems. Automatic machinery (General), adaptive modelling, TJ212-225, Nonlinear rational model, 0202 electrical engineering, electronic engineering, information engineering, bias compensation, stochastic gradient algorithm, 02 engineering and technology, parameter estimation

Popis souboru: application/pdf

Přístupová URL adresa: https://doaj.org/article/9c579847d3df43b79066e24d39572826

Fractional Stochastic Gradient Algorithm for Time-Delayed Models With Piece-Wise Linear Input Using Self-Organizing Maps Method

Autoři: Jia Tang

Zdroj: IEEE Access, Vol 10, Pp 131053-131060 (2022)

Témata: 0209 industrial biotechnology, nonlinear model, fractional stochastic gradient algorithm, self-organizing maps, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, System identification, multi-innovation, piece-wise linear, TK1-9971

Přístupová URL adresa: https://doaj.org/article/50b00c500aa5491d9494c9e37117968d

Improved Redundant Rule-Based Stochastic Gradient Algorithm for Time-Delayed Models Using Lasso Regression

Autoři: Hangtao Zhao Lixin Lv Yuejiang Ji

Zdroj: IEEE Access, Vol 10, Pp 3336-3342 (2022)

Témata: lasso regression, Parameter estimation, 0202 electrical engineering, electronic engineering, information engineering, stochastic gradient algorithm, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, redundant rule, time-delayed model, TK1-9971

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/6514899/09663158.pdf
https://doaj.org/article/1bf92502cb384635a54ab371169cdfa2

An investigation of stochastic trust-region based algorithms for finite-sum minimization

Autoři: Bellavia, Stefania Morini, Benedetta Rebegoldi, Simone

Přispěvatelé: Bellavia, Stefania Morini, Benedetta Rebegoldi, Simone

Témata: adaptive sampling strategie, Finite-sum minimization, stochastic gradient algorithm, trust-region

Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001286818100001; firstpage:1; lastpage:30; journal:OPTIMIZATION METHODS & SOFTWARE; https://hdl.handle.net/11380/1351026

Dostupnost: https://hdl.handle.net/11380/1351026
https://doi.org/10.1080/10556788.2024.2346834

Malliavin Calculus with Weak Derivatives for Counterfactual Stochastic Optimization

Autoři: Krishnamurthy, Vikram Snow, Luke

Témata: Malliavin derivative, Weak derivative estimator, Neural stochastic differential equation, Stochastic gradient algorithm

Relation: 2025 61st Allerton Conference on Communication, Control, and Computing Proceedings; https://hdl.handle.net/2142/130297&&; http://hdl.handle.net/2142/130297

Dostupnost: https://hdl.handle.net/2142/130297&&<https://hdl.handle.net/2142/130297>
http://hdl.handle.net/2142/130297

Maximum likelihood identification of dual‐rate Hammerstein output‐error moving average system: Maximum likelihood identification of dual-rate Hammerstein output-error moving average system

Autoři: Jiali Zhang Junhong Li

Zdroj: IET Control Theory & Applications. 14:1089-1101

Témata: 0209 industrial biotechnology, Estimation and detection in stochastic control theory, Identification in stochastic control theory, optimisation, polynomials, Hammerstein output-error moving average system, maximum likelihood multi-innovation forgetting factor stochastic gradient identification algorithm, stochastic gradient optimisation method, dual-rate sampled systems, maximum likelihood estimation, 02 engineering and technology, least squares approximations, maximum likelihood principle, 0202 electrical engineering, electronic engineering, information engineering, stochastic gradient algorithm, identification model, maximum likelihood identification, multiple innovation identification theory, Newton optimisation, dual-rate sampled data, gradient search method, Hammerstein dual-rate model, polynomial transformation technique, water tank system, Least squares and related methods for stochastic control systems, stochastic processes, parameter estimation, stochastic process theory, gradient methods

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/7907180
https://doi.org/10.1049/iet-cta.2019.0419
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/iet-cta.2019.0419
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2019.0419
https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2019.0419

Adaptive Filtering Under the Maximum Correntropy Criterion With Variable Center

Autoři: Lingfei Zhu Chengtian Song Lizhi Pan a další

Zdroj: IEEE Access, Vol 7, Pp 105902-105908 (2019)

Témata: maximum correntropy criterion with variable center(MCC-VC), 0202 electrical engineering, electronic engineering, information engineering, stochastic gradient algorithm, steady-state excess mean square error, Electrical engineering. Electronics. Nuclear engineering, 02 engineering and technology, Adaptive filtering, TK1-9971

Přístupová URL adresa: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08782582.pdf
https://doaj.org/article/df61cf84a88f451fa5ee189063293e45
https://dblp.uni-trier.de/db/journals/access/access7.html#ZhuSPL19
https://doi.org/10.1109/ACCESS.2019.2932201
https://ieeexplore.ieee.org/document/8782582

A Stochastic Gradient Method With Mesh Refinement for PDE-Constrained Optimization Under Uncertainty

Autoři: Caroline Geiersbach Winnifried Wollner

Zdroj: SIAM Journal on Scientific Computing. 42:A2750-A2772

Témata: ddc:510, 49M25, article, 0211 other engineering and technologies, Numerical Analysis (math.NA), 02 engineering and technology, stochastic approximation -- stochastic gradient algorithm -- random elliptic PDEs as constraints -- PDE-constrained optimization under uncertainty -- optimization in Hilbert spaces -- discretization error, 01 natural sciences, Optimization and Control (math.OC), 35Q93, FOS: Mathematics, 60H35, Mathematics - Numerical Analysis, 62L20, 0101 mathematics, Mathematics - Optimization and Control

Přístupová URL adresa: http://arxiv.org/pdf/1905.08650
http://arxiv.org/abs/1905.08650
http://export.arxiv.org/pdf/1905.08650
https://arxiv.org/pdf/1905.08650.pdf
https://arxiv.org/abs/1905.08650
https://epubs.siam.org/doi/10.1137/19M1263297
http://ui.adsabs.harvard.edu/abs/2019arXiv190508650G/abstract
http://doi.org/10.1137/19M1263297
https://dblp.uni-trier.de/db/journals/siamsc/siamsc42.html#GeiersbachW20
https://doi.org/10.1137/19M1263297

A stochastic multiple gradient descent algorithm

Autoři: Quentin Mercier Fabrice Poirion Jean-Antoine Désidéri a další

Přispěvatelé: Quentin Mercier Fabrice Poirion Jean-Antoine Désidéri a další

Zdroj: European Journal of Operational Research. 271:808-817

Témata: MULTIPLE GRADIENT DESCENT ALGORITHM, COMMON DESCENT VECTOR, STOCHASTIC GRADIENT ALGORITHM, INCERTITUDE, MULTIOBJECTIVE STOCHASTIC OPTIMIZATION, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, MULTIPLE OBJECTIVE PROGRAMMING, 02 engineering and technology, [SPI.MAT] Engineering Sciences [physics]/Materials, OPTIMISATION MULTIOBJECTIF

Popis souboru: application/pdf

Přístupová URL adresa: https://hal.archives-ouvertes.fr/hal-01833165/file/DMAS18110.1529998080_postprint.pdf
https://hal.science/hal-01833165v1
https://hal.science/hal-01833165v1/document
https://doi.org/10.1016/j.ejor.2018.05.064
https://hal.archives-ouvertes.fr/hal-01833165/document
https://doi.org/10.1016/j.ejor.2018.05.064
https://dblp.uni-trier.de/db/journals/eor/eor271.html#MercierPD18
https://ideas.repec.org/a/eee/ejores/v271y2018i3p808-817.html
https://hal.archives-ouvertes.fr/hal-01833165
https://www.sciencedirect.com/science/article/pii/S0377221718304831

The value of collaboration in convex machine learning with differential privacy

Autoři: Wu, Nan Farokhi, Farhad Smith, David a další

Zdroj: Wu , N , Farokhi , F , Smith , D & Kaafar , M A 2020 , The value of collaboration in convex machine learning with differential privacy . in Proceedings - 2020 IEEE Symposium on Security and Privacy, SP 2020 . Proceedings - IEEE Symposium on Security and Privacy , vol. 2020-May , Los Alamitos, CA , pp. 304-317 , 41st IEEE Symposium on Security and Privacy, SP 2020 , San Francisco , United States , 18/05/20 . https://doi.org/10.1109/SP40000.2020.00025

Témata: Differential privacy, Machine learning, Stochastic gradient algorithm

Relation: urn:ISBN:9781728134970

Dostupnost: https://researchers.mq.edu.au/en/publications/3b9b7440-eb91-49c4-b254-ab5df0fed996
https://doi.org/10.1109/SP40000.2020.00025
http://www.scopus.com/inward/record.url?scp=85084279655&partnerID=8YFLogxK

Deep Learning for Ordinary Differential Equations and Predictive Uncertainty

Autoři: Yijia Liu

Témata: Deep learning, Statistics not elsewhere classified, Ordinary Differential Equations (ODE), Stochastic Gradient Algorithm, Empirical Bayes, Deep Neural Networks

Relation: https://figshare.com/articles/thesis/Deep_Learning_for_Ordinary_Differential_Equations_and_Predictive_Uncertainty/25621530

Dostupnost: https://doi.org/10.25394/pgs.25621530.v1

Convergence of the recursive identification algorithms for multivariate pseudo‐linear regressive systems: Convergence of the recursive identification algorithms for multivariate pseudo-linear regressive systems

Autoři: Xuehai Wang Feng Ding

Zdroj: International Journal of Adaptive Control and Signal Processing. 30:824-842

Témata: Systems theory, control, pseudo-linear regressive model, stochastic gradient algorithm, martingale convergence theorem, parameter estimation, 16. Peace & justice, least squares algorithm

Popis souboru: application/xml

Přístupová URL adresa: https://zbmath.org/8036807
https://doi.org/10.1002/acs.2642
https://dlnext.acm.org/doi/10.1002/acs.2642
https://onlinelibrary.wiley.com/doi/10.1002/acs.2642

Parameter Estimation of a Class of Neural Systems with Limit Cycles

Autoři: Xuyang Lou Xu Cai Baotong Cui

Zdroj: Algorithms, Vol 11, Iss 11, p 169 (2018)

Témata: parameter estimation, neural system, recursive least-squares algorithm, stochastic gradient algorithm, innovation, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95

Relation: https://www.mdpi.com/1999-4893/11/11/169; https://doaj.org/toc/1999-4893; https://doaj.org/article/e7791a336b4d4ec3bd739f0cdec70447

Dostupnost: https://doi.org/10.3390/a11110169
https://doaj.org/article/e7791a336b4d4ec3bd739f0cdec70447

A stochastic gradient method with mesh refinement for PDE constrained optimization under uncertainty

Autoři: Geiersbach, Caroline Wollner, Winnifried

Témata: article, ddc:510, 62L20, 35Q93, 60H35, 49M25, Nichtglatte Variationsprobleme und Operatorgleichungen, Optimale Steuerung partieller Differentialgleichungen und nichtlineare Optimierung, stochastic approximation -- stochastic gradient algorithm -- random elliptic PDEs as constraints -- PDE-constrained optimization under uncertainty -- optimization in Hilbert spaces -- discretization error

Relation: https://doi.org/10.48550/arXiv.1905.08650; https://arxiv.org/abs/1905.08650

Dostupnost: https://doi.org/10.48550/arXiv.1905.08650
https://archive.wias-berlin.de/receive/wias_mods_00007797
https://archive.wias-berlin.de/servlets/MCRFileNodeServlet/wias_derivate_00004120/1905.08650v3.pdf
https://arxiv.org/abs/1905.08650