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adaptive forgetting factor Subarray RLS Beamforming for Multipath Environments

IEICE TRANSACTIONS ON COMMUNICATIONS 2008年第10期E91B卷 3342-3346页

作者： Chang, Ann-Chen Hsu, Chun Su, Ing-Jiunn Ling Tung Univ Dept Informat Technol Taichung 408 Taiwan Natl Def Univ Chung Cheng Inst Technol Dept Elect & Elect Engn Tao Yuan 335 Taiwan

This letter presents an efficient adaptive beamformer to deal with the multipath environments created by signal source scatterings. To improve the performance possible with the fixed forgetting factor. the regular adaptive forgetting factor algorithm is derived and applied to the subarray recursive least squares (RLS) beamforming. Simulations confirm that the proposed scheme has better performance than not only the conventional RLS algorithm but also the subarray RLS and adaptive forgetting factor RLS algorithms.

关键词： adaptive beamforming recursive least squares generalized sidelobe canceller adaptive forgetting factor

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Online Sequential Model for Multivariate Time Series Prediction With adaptive forgetting factor

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IEEE ACCESS 2020年 8卷 175958-175971页

作者： Dai, Jinling Xu, Aiqiang Liu, Xing Yu, Chao Wu, Yangyong Naval Aviat Univ Informat & Commun Engn Yantai 264001 Peoples R China Naval Aviat Univ Dept Sci Res Yantai 264001 Peoples R China Naval Aviat Univ Mil Equipment Yantai 264001 Peoples R China Naval Aviat Univ Instrument Sci & Technol Yantai 264001 Peoples R China 92313 Troops Jiyuan 454650 Peoples R China

In the process of online prediction of multivariable non-stationary time series by kernel extreme learning machine (KELM), the dynamic characteristics of the system which are difficult to determine have always posed a big problem. We propose an online sequential prediction model with an adaptive forgetting factor (AFF) for multivariable time series to solve this problem. The multivariable time series instead of variable itself is reconstructed firstly. AFF is introduced into the objective function and can be adjusted iteratively and adaptively with the system changes. As a result, higher weight can be allocated for the fresh and more important samples while the old failure samples can be quickly forgotten. The model sparsification uses a fast leave-one-out cross-validation (FLOO-CV) method to set a prediction error threshold so that samples can be selected conditionally to form a dictionary. Besides, the dictionary parameters, including AFF and kernel parameters, are recursively updated simultaneously without increasing calculation complexity. The experimental results show that, compared with four fashionable KLEM methods, the proposed AFF-OSKELM has a better dynamic tracking ability and adaptability. Moreover, compared with single variable prediction, the spatial reconstructed multivariable has higher prediction accuracy and stability.

关键词： Time series analysis Adaptation models Kernel Predictive models Dictionaries Computational modeling Complexity theory Kernel extreme learning machine adaptive forgetting factor fast leave-one-out cross-validation online prediction multivariable time series

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The research of superheated steam temperature control based on generalized predictive control algorithm and adaptive forgetting factor

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INTERNATIONAL JOURNAL OF adaptive CONTROL AND SIGNAL PROCESSING 2020年第1期34卷 15-31页

作者： Jiang, Cheng Qian, Hong Pan, Yuekai Chai, Tingting Shanghai Univ Elect Power Shanghai 200090 Peoples R China Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

The superheated steam temperature system of the thermal power plant has the characteristics of large inertia, nonlinearity, and strong time variation, which make it difficult to be controlled. To address these problems, this paper proposes a generalized predictive control algorithm with an adaptive forgetting factor. First, based on a fuzzy algorithm and a recursive least squares algorithm, the controlled object's model can be quickly and accurately obtained with the adaptive forgetting factor in real time. It overcomes the nonlinear and time-varying problems of the controlled object in the control progress. Meanwhile, it also solves the problem of data saturation and the weight assignment of the "new and old" data during online identification. Second, an adaptive generalized predictive controller algorithm has been developed with the controlled object. It solves the large inertia problem of the controlled object. Finally, through establishing simulation model of the superheated steam temperature system and simulating, the results show that the proposed method has better control performance, antidisturbance ability, adaptability, and robustness. Moreover, it has a certain reference significance for the design of a practical control system.

关键词： adaptive forgetting factor fuzzy algorithm generalized predictive control recursive least squares algorithm superheated steam temperature

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DASH Adaptation Algorithm Based on adaptive forgetting factor Estimation

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第5期20卷 1224-1232页

作者： Aguayo, Miguel Bellido, Luis Lentisco, Carlos M. Pastor, Encarna Univ Politecn Madrid Dept Telemat Syst Engn E-28040 Madrid Spain

The wide adoption of multimedia service-capable mobile devices, the availability of better networks with higher bandwidths, and the availability of platforms offering digital content has led to an increasing popularity of multimedia streaming services. However, multimedia streaming services can be subject to different factors that affect the quality perceived by the users, such as service interruptions or quality oscillations due to changing network conditions, particularly in mobile networks. Dynamic adaptive Streaming over HTTP (DASH), leverages the use of content-distribution networks and the capabilities of the multimedia devices to allow multimedia players to dynamically adapt the quality of the media streaming to the available bandwidth and the device characteristics. While many elements of DASH are standardized, the algorithms providing the dynamic adaptation of the streaming are not. The adaptation is often based on the estimation of the throughput or a buffer control mechanism. In this paper, we present a new throughput estimation adaptation algorithm based on a statistical method named adaptive forgetting factor (AFF). Using this method, the adaptation logic is able to react appropriately to the different conditions of different types of networks. A set of experiments with different traffic profiles show that the proposed algorithm improves video quality performance in both wired and wireless environments.

关键词： adaptive streaming over HTTP adaptive forgetting factor mobile communication multimedia content delivery throughput estimation

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RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第4期7卷 507-515页

作者： LIANG Yong-ming LUO Han-wen HUANG Jian-guo Department of Electronic Engineering Shanghai Jiao Tong University Shanghai 200030 China College of Marine Engineering Northwestern Polytechnical University. Xi＇an 710072 China

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

关键词： MIMO-OFDM Channel estimation RLS algorithm adaptive forgetting factor

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adaptive forgetting factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

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ENERGIES 2019年第12期12卷 2242-2242页

作者： Sun, Xiangdong Ji, Jingrun Ren, Biying Xie, Chenxue Yan, Dan Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Shaanxi Peoples R China

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

关键词： lithium-ion battery equivalent circuit model recursive least square adaptive forgetting factor parameter identification

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RLS CMA Blind Equalization with adaptive forgetting factor Controlled by Energy Steady State 9

RLS CMA Blind Equalization with Adaptive Forgetting Factor C...

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9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Xiao, Ying Sun, Jin-yu Dalian Nationality Univ Coll Informat & Commun Engn Dalian Liaoning Peoples R China

ISBN: (纸本)9781509037100

To further improve the performance of recurrent least square (RLS) constant modulus algorithm (CMA) RLS-CMA blind equalization, an RLS-CMA blind equalization algorithm with adaptive forgetting factor controlled by energy steady state was proposed. The cost function of CMA is simplified to meet second normal form, and the blind equalizer can be updated according to RLS algorithm. The energy rate of the blind equalizer is defined, based on which to judge the algorithm enter the steady state. The forgetting factor switches from the small value to big value when the steady state is reached. There are only two forgetting factors can be selected during the iterative process, which can take full advantages of small and big forgetting factors in the RLS algorithm. Computer simulation results show the effectiveness of the proposed algorithm.

关键词： blind euqalization CMA RLS adaptive forgetting factor steady state

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Diversity-based diffusion robust RLS using adaptive forgetting factor

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SIGNAL PROCESSING 2021年 182卷 107950-107950页

作者： Sadigh, Alireza Naeimi Yazdi, Hadi Sadoghi Harati, Ahad Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Razavi Khorasan Iran

In this study, we propose a diffusion robust recursive least squares (D-(RLS)-L-2) algorithm over adaptive networks. Instead of conventional mean square error cost function, the suggested method is derived from the maximum correntropy criterion (MCC) cost function, being more suitable for non-Gaussian noise. Furthermore, to improve tracking ability when encountering sudden changes in unknown systems in nonstationary environments, a diversity-based extension of D-(RLS)-L-2 is developed by adaptive forgetting factor for each node. Also, to conduct performance analysis, we employ a half-quadratic optimization to approximate our model iteratively by a quadratic problem. The mean, mean-square convergence and stability of the D-(RLS)-L-2 are discussed theoretically. The simulation results show that the proposed methods outperform the other robust algorithms and enhance tracking quality in the presence of non-Gaussian noise in the stationary and non-stationary environments. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Diffusion robust recursive least squares Half-quadratic optimization Diversity adaptive forgetting factor Performance analysis

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RLS CMA Blind Equalization with adaptive forgetting factor Controlled by Energy Steady State

RLS CMA Blind Equalization with Adaptive Forgetting Factor C...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Ying XIAO Jin-yu SUN College of Information and Communication Engineering Dalian Nationality University

ISBN: (纸本)9781509037117

关键词： steady state blind equalization CMA RLS adaptive forgetting factor

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Autonomous navigation method of satellite constellation based on adaptive forgetting factors

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Chinese Journal of Aeronautics 2024年第7期37卷 317-332页

作者： Dong WANG Jing YANG Kai XIONG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control EngineeringBeijing 100094China

To address the problem that model uncertainty and unknown time-varying system noise hinder the filtering accuracy of the autonomous navigation system of satellite constellation,an autonomous navigation method of satellite constellation based on the Unscented Kalman Filter with adaptive forgetting factors(UKF-AFF)is *** process noise covariance matrix is estimated online with the strategy that combines covariance matching and adaptive adjustment of forgetting *** adaptive adjustment coefficient based on squared Mahalanobis distance of state residual is employed to achieve online regulation of forgetting factors,equipping this method with more *** intersatellite direction vector obtained from photographic observations is introduced to determine the constellation satellite orbit together with the distance measurement to avoid rank deficiency *** that the number of available measurements varies online with intersatellite visibility in practical applications such as time-varying constellation configurations,the smooth covariance matrix of state correction determined by innovation and gain is adopted and constructed *** analysis of the proposed method is also *** effectiveness of the proposed method is verified by the Monte Carlo simulation and comparison *** estimation accuracy of constellation position and velocity of UKF-AFF is improved by 30%and 44%respectively compared to those of the extended Kalman filter,and the method proposed is also better than other several adaptive filtering methods in the presence of significant model uncertainty.

关键词： Constellation autonomous navigation Unscented Kalman filter adaptive forgetting factor Model uncertainty Stability analysis

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