Výsledky vyhľadávania - "stochastic gradient algorithms"

Auxiliary model‐based recursive least squares and stochastic gradient algorithms and convergence analysis for feedback nonlinear output‐error systems Autor Miao, Guangqin, Yang, Dan, Ding, Feng

“… The auxiliary model‐based recursive least squares algorithm and the auxiliary model‐based stochastic gradient algorithm are derived for parameter estimation…”
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Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems Autor Wei, Chun, Zhang, Xiao, Xu, Ling, Ding, Feng, Yang, Erfu

“…‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate…”
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Parallel stochastic gradient algorithms for large-scale matrix completion Autor Recht, Benjamin, Ré, Christopher

“…This paper develops Jellyfish , an algorithm for solving data-processing problems with matrix-valued decision variables regularized to have low rank…”
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Multikernel Passive Stochastic Gradient Algorithms and Transfer Learning Autor Krishnamurthy, Vikram, Yin, George

“…This article develops a novel passive stochastic gradient algorithm. In passive stochastic approximation, the stochastic gradient algorithm does not have control over the location where noisy gradients of the cost function are evaluated…”
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Hierarchical maximum likelihood generalized extended stochastic gradient algorithms for bilinear‐in‐parameter systems Autor Liu, Haibo, Wang, Junwei, Meng, Xiangxiang

“…‐in‐parameter system with autoregressive moving average noise. A maximum likelihood multi‐innovation stochastic gradient algorithm is derived to estimate the model…”
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Two improved generalized extended stochastic gradient algorithms for CARARMA systems Autor Lv, Lingling, Zhang, Yulin, Huang, Quanzhen, Wu, Yu

“…The paper innovatively proposes two improved generalized extended stochastic gradient (GESG) algorithms for the controlled autoregressive autoregressive moving…”
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Fractional-Based Stochastic Gradient Algorithms for Time-Delayed ARX Models Autor Xu, Tianyang, Chen, Jing, Pu, Yan, Guo, Liuxiao

“… These two FSG algorithms have faster convergence rates when compared with the stochastic gradient algorithm…”
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Adaptive Time Synchronization in Time Sensitive-Wireless Sensor Networks Based on Stochastic Gradient Algorithms Framework Autor Abdul-Rashid, Ramadan, Chan, Kar Tim, Rahman, Mohd Amiruddin Abd, Sangaiah, Arun Kumar

“…This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms…”
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Estimating the geometric median in Hilbert spaces with stochastic gradient algorithms: L p and almost sure rates of convergence Autor Godichon-Baggioni, Antoine

“… of the geometric median based on such non linear stochastic gradient algorithms. The $L^p$ rates of convergence as well as almost sure rates of convergence of these estimators are derived in general separable Hilbert spaces…”
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Online estimation of the asymptotic variance for averaged stochastic gradient algorithms Autor Godichon, Antoine

“…Stochastic gradient algorithms are more and more studied since they can deal efficiently and online with large samples in high dimensional spaces…”
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Some Stochastic Gradient Algorithms for Hammerstein Systems with Piecewise Linearity Autor Pu, Yan, Yang, Yongqing, Chen, Jing

“…Some stochastic gradient (SG) algorithms for Hammerstein systems with piecewise linearity are developed in this paper. Due to the complexity of the nonlinear…”
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On Distributed Stochastic Gradient Algorithms for Global Optimization Autor Swenson, Brian, Sridhar, Anirudh, Poor, H. Vincent

“…The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed…”
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Multi-innovation stochastic gradient algorithms for multi-input multi-output systems Autor Han, Lili, Ding, Feng

“…This paper develops a multi-innovation stochastic gradient (MISG) algorithm for multi-input multi-output systems by expanding the innovation vector to an…”
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Performance analysis of stochastic gradient algorithms under weak conditions Autor Ding, Feng, Yang, HuiZhong, Liu, Fei

“…By using the stochastic martingale theory, convergence properties of stochastic gradient （SG） identification algorithms are studied under weak conditions…”
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On the rates of convergence of parallelized averaged stochastic gradient algorithms Autor Godichon-Baggioni, Antoine, Saadane, Sofiane

“… We introduce here a parallelized averaged stochastic gradient algorithm, which enables to treat efficiently and recursively the data, and so, without taking care if the distribution of the data…”
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RETRACTED ARTICLE: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient Autor Godichon-Baggioni, Antoine

“…: Antoine Godichon-Baggioni. Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient. Statistics. 57:3. 2023. 637-668. doi: 10.1080…”
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Statement of Retraction: Convergence in quadratic mean of averaged stochastic gradient algorithms without strong convexity nor bounded gradient

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A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms With Exponentiated Error Cost Functions Autor Luo, Zhengyan, Zhao, Haiquan, Zeng, Xiangping

“…In this paper, a class of diffusion zero-attracting stochastic gradient algorithms with exponentiated error cost functions is put forward due to its good performance for sparse system identification…”
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The residual based interactive stochastic gradient algorithms for controlled moving average models Autor Wang, Lingyun, Xie, Li, Wang, Xianfang

“… Compared with the residual based extended stochastic gradient algorithm for identifying CMA models, the proposed ISG algorithm can give highly accurate parameter…”
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The Limitation and Practical Acceleration of Stochastic Gradient Algorithms in Inverse Problems Autor Tang, Junqi, Egiazarian, Karen, Davies, Mike

“…In this work we investigate the practicability of stochastic gradient descent and recently introduced variants with variance-reduction techniques in imaging…”
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