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Adaptive Time Synchronization in Time Sensitive-Wireless Sensor Networks Based on Stochastic Gradient Algorithms Framework 认领 引用 被引量：2
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作者 Ramadan Abdul-Rashid Mohd Amiruddin Abd Rahman +1 位作者 Kar Tim Chan Arun Kumar Sangaiah 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第3期2585-2616,共32页
This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different... This study proposes a novel time-synchronization protocol inspired by stochastic gradient algorithms.The clock model of each network node in this synchronizer is configured as a generic adaptive filter where different stochastic gradient algorithms can be adopted for adaptive clock frequency adjustments.The study analyzes the pairwise synchronization behavior of the protocol and proves the generalized convergence of the synchronization error and clock frequency.A novel closed-form expression is also derived for a generalized asymptotic error variance steady state.Steady and convergence analyses are then presented for the synchronization,with frequency adaptations done using least mean square(LMS),the Newton search,the gradient descent(GraDes),the normalized LMS(N-LMS),and the Sign-Data LMS algorithms.Results obtained from real-time experiments showed a better performance of our protocols as compared to the Average Proportional-Integral Synchronization Protocol(AvgPISync)regarding the impact of quantization error on synchronization accuracy,precision,and convergence time.This generalized approach to time synchronization allows flexibility in selecting a suitable protocol for different wireless sensor network applications. 展开更多
关键词 Wireless sensor network time synchronization stochastic gradient algorithm multi-hop
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A stochastic gradient-based two-step sparse identification algorithm for multivariate ARX systems 认领 引用
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作者 Yanxin Fu Wenxiao Zhao 《Control Theory and Technology》 EI CSCD 2024年第2期213-221,共9页
We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (... We consider the sparse identification of multivariate ARX systems, i.e., to recover the zero elements of the unknown parameter matrix. We propose a two-step algorithm, where in the first step the stochastic gradient (SG) algorithm is applied to obtain initial estimates of the unknown parameter matrix and in the second step an optimization criterion is introduced for the sparse identification of multivariate ARX systems. Under mild conditions, we prove that by minimizing the criterion function, the zero elements of the unknown parameter matrix can be recovered with a finite number of observations. The performance of the algorithm is testified through a simulation example. 展开更多
关键词 ARX system Stochastic gradient algorithm Sparse identification Support recovery Parameter estimation Strong consistency
Auxiliary Model Based Multi-innovation Stochastic Gradient Identification Methods for Hammerstein Output-Error System 认领 引用
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作者 冯启亮 贾立 李峰 《Journal of Donghua University(English Edition)》 CAS 2017年第1期53-59,共7页
Special input signals identification method based on the auxiliary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed.The special input signals were used to rea... Special input signals identification method based on the auxiliary model based multi-innovation stochastic gradient algorithm for Hammerstein output-error system was proposed.The special input signals were used to realize the identification and separation of the Hammerstein model.As a result,the identification of the dynamic linear part can be separated from the static nonlinear elements without any redundant adjustable parameters.The auxiliary model based multi-innovation stochastic gradient algorithm was applied to identifying the serial link parameters of the Hammerstein model.The auxiliary model based multi-innovation stochastic gradient algorithm can avoid the influence of noise and improve the identification accuracy by changing the innovation length.The simulation results show the efficiency of the proposed method. 展开更多
关键词 Hammerstein output-error system special input signals auxiliary model based multi-innovation stochastic gradient algorithm innovation length
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Multi-channel blind deconvolution algorithm for multiple-input multiple-output DS/CDMA system 认领 引用
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作者 Cheng Hao Guo Wei Jiang Yi 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第3期454-461,共8页
Direct sequence spread spectrum transmission can be realized at low SNR,and has low probabilityof detection.It is aly problem how to obtain the original users'signal in a non-cooperative context.In practicality,th... Direct sequence spread spectrum transmission can be realized at low SNR,and has low probabilityof detection.It is aly problem how to obtain the original users'signal in a non-cooperative context.In practicality,the DS/CDMA sources received are linear convolute mixing.A more complex multichannel blind deconvolution MBD algorithm is required to achieve better source separation.An improved MBD algorithm for separating linear convolved mixtures of signals in CDMA system is proposed.This algorithm is based on minimizing the average squared cross-output-channel-correlation.The mixture coefficients are totally unknown,while some knowledge about temporal model exists.Results show that the proposed algorithm can bring about the exactness and low computational complexity. 展开更多
关键词 DS/CDMA signal non-cooperative MBD stochastic gradient algorithms for MBD.
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Dual-wavelength coherent beam combination and wide-angle beam steering via integrated optical phased arrays 认领 引用
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作者 Xiaobin Wang Yuqiu Zhang +9 位作者 Qiyu Wang Xu Zhao Jingye Chen Hongxiang Chang Zhongquan Nie Jinhai Zou Jian Wu Jinyong leng Pu Zhou Xiang’e Han 《Advanced Photonics Nexus》 CSCD 2026年第4期248-258,共11页
Integrated optical phased arrays(OPAs),owing to their high integration level and wide-angle beamsteering capability,are a key promising component for 3D/4D sensing and free-space optical communication.However,integrat... Integrated optical phased arrays(OPAs),owing to their high integration level and wide-angle beamsteering capability,are a key promising component for 3D/4D sensing and free-space optical communication.However,integrated OPAs typically employ a single-wavelength laser as the input light source,which limits their application in spectral imaging and secure communication.In this work,we propose and experimentally demonstrate a dual-wavelength coherent beam combining(CBC)scheme using a 64-element integrated OPA.A multiwavelength CBC model is established and validated with two wavelength pairs(1545/1555 and 1535∕1565 nm).Both simulation and experimental results confirm that the beam dispersion intensifies with the steering angle and wavelength separation.To overcome this inherent dispersion issue,we employ a stochastic parallel gradient descent algorithm with two avalanche photodiodes as feedback sensors,achieving an arbitrarily configurable angular separation between the two wavelength beams.Finally,we demonstrate the wide field of view beam-steering capability of the combined beam across a 60 deg(±30 deg)range.We provide a foundational framework for advancing multiwavelength OPA toward applications in spectral imaging and secure optical communications. 展开更多
关键词 integrated optical phased arrays coherent beam combination dual-wavelength stochastic parallel gradient descent algorithm
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Distributed Byzantine-Resilient Learning of Multi-UAV Systems via Filter-Based Centerpoint Aggregation Rules 认领 引用 被引量：2
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作者 Yukang Cui Linzhen Cheng +1 位作者 Michael Basin Zongze Wu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第5期1056-1058,共3页
Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication w... Dear Editor,Through distributed machine learning,multi-UAV systems can achieve global optimization goals without a centralized server,such as optimal target tracking,by leveraging local calculation and communication with neighbors.In this work,we implement the stochastic gradient descent algorithm(SGD)distributedly to optimize tracking errors based on local state and aggregation of the neighbors'estimation.However,Byzantine agents can mislead neighbors,causing deviations from optimal tracking.We prove that the swarm achieves resilient convergence if aggregated results lie within the normal neighbors'convex hull,which can be guaranteed by the introduced centerpoint-based aggregation rule.In the given simulated scenarios,distributed learning using average,geometric median(GM),and coordinate-wise median(CM)based aggregation rules fail to track the target.Compared to solely using the centerpoint aggregation method,our approach,which combines a pre-filter with the centroid aggregation rule,significantly enhances resilience against Byzantine attacks,achieving faster convergence and smaller tracking errors. 展开更多
关键词 global optimization goals multi UAV systems filter based centerpoint aggregation distributed learning optimal target trackingby stochastic gradient descent algorithm sgd distributedly optimize tracking distributed machine learningmulti uav
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Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion 认领 引用 被引量：3
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作者 Sheng Chen 《International Journal of Automation and computing》 2006年第3期291-303,共13页
Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative ad... Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-Gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach. 展开更多
关键词 Adaptive filtering mean square error probability density function non-Gaussian distribution Parzen window estimate symbol error rate stochastic gradient algorithm.
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Coherent beam combining of hybrid phase control in master oscillator-power amplifier configuration 认领 引用 被引量：3
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作者 王小林 周朴 +4 位作者 马阎星 马浩统 许晓军 刘泽金 赵伊君 《Chinese Physics B》 SCIE EI CAS 2010年第9期374-378,共5页
A novel scalable architecture for coherent beam combining with hybrid phase control involving passive phasing and active phasing in master oscillator-power amplifier configuration is presented. Wide-linewidth mutually... A novel scalable architecture for coherent beam combining with hybrid phase control involving passive phasing and active phasing in master oscillator-power amplifier configuration is presented. Wide-linewidth mutually injected passive phasing fibre laser arrays serve as master oscillators for the power amplifiers, and the active phasing using stochastic parallel gradient descent algorithm is induced. Wide-linewidth seed laser can suppress the stimulated Brillouin scattering effectively and improve the output power of the fibre laser amplifier, while hybrid phase control provides a robust way for in-phase mode coherent beam combining simultaneously. Experiment is performed by active phasing fibre laser amplifiers with passive phasing fibre ring laser array seed lasers. Power encircled in the main-lobe increases1.57 times and long-exposure fringe contrast is obtained to be 78% when the system evolves from passive phasing to hybrid phasing. 展开更多
关键词 fibre laser coherent beam combining hybrid phase control stochastic parallel gradient descent algorithm
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Convergence of Recursive Identification for ARMAX Process with Increasing Variances 认领 引用
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作者 金亚 罗贵明 《Tsinghua Science and Technology》 EI CAS 2007年第1期38-43,共6页
The autoregressive moving average exogenous （ARMAX） model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inpu... The autoregressive moving average exogenous （ARMAX） model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average （MA） part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results. 展开更多
关键词 multidimensional autoregressive moving average exogenous （ARMAX） process increasing variance stochastic gradient algorithm convergence
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