检索结果-内蒙古大学图书馆

minimum error entropy with affine projection algorithm for robust adaptive filtering

DIGITAL SIGNAL PROCESSING 2025年 163卷

作者： Zhang, Huaiyuan Li, Guoliang Hou, Yunxian Zhang, Hongbin Zhong, Shan Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 610000 Peoples R China Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 610000 Peoples R China

Considering the similarity and correlation between the method of affine projection (AP) and minimum error entropy (MEE), we find that AP and MEE can be combined well, so a new robust adaptive filtering algorithm named APMEE was proposed. By optimizing the non-parametric estimator of quadratic Renyi's entropy of two individual errors in an observation interval with a l(2)-norm constraint on the weight vector, APMEE greatly improves the convergence speed and steady-state accuracy of adaptive filtering. Numerical simulation experiments of system identification and echo cancellation indicate that APMEE outperforms other algorithms based on affine projection.

关键词： Adaptive filter minimum error entropy Affine projection System identification Echo cancellation

来源：评论

学校读者我要写书评

暂无评论

minimum error entropy Estimation Under Contaminated Gaussian Noise

引用

IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 1457-1461页

作者： Lopez, Carlos Alejandro de Cabrera, Ferran Riba, Jaume Univ Politecn Catalunya UPC Dept Teoria Senyal & Comunicac Signal Proc & Commun Grp SPCOM Barcelona 08034 Spain

is shown that R & eacute;nyi's entropy of a Gaussian mixture with entropic index alpha is an element of (1, infinity] is upper-bounded by the cluster with minimum variance. This basic idea leads to a clean worst-case formulation of the minimum error entropy principle in the context of linear multi-sensor fusion by using a largely contaminated Gaussian distribution to model sensor errors with outliers. The obtained entropic best linear unbiased estimator leads to an operational interpretation in terms of a precision/reliability trade-off, it resonates closely with model-order selection methods, and it provides a possible information-theoretic root to sparsity promoting regularization.

关键词： minimum error entropy R & eacute nyi's entropy concavity deficit sparsity-promoting regularization model-order selection model-order selection model-order selection model-order selection

来源：评论

学校读者我要写书评

暂无评论

Anomaly-Resistant Decentralized State Estimation Under minimum error entropy With Fiducial Points for Wide-Area Power Systems

引用

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli Dong Hongjian Liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

来源：评论

学校读者我要写书评

暂无评论

Adaptive Kalman Filter Under minimum error entropy with Fiducial Points for Strap-Down Inertial Navigation System/Ultra-Short Baseline Integrated Navigation Systems

引用

JOURNAL OF MARINE SCIENCE AND ENGINEERING 2025年第5期13卷 990-990页

作者： Wang, Boyang Wang, Zhenjie China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

The integration of strap-down inertial navigation systems (SINSs) and ultra-short baseline (USBL) systems has become a mainstream navigation approach for unmanned underwater vehicles (UUVs). In shallow-sea environments, USBL measurements are frequently affected by complex non-Gaussian disturbances. Under such challenging conditions, traditional Kalman filters often exhibit limited performance in maintaining navigation accuracy. A novel adaptive Kalman filter is proposed to address this issue. The proposed method demonstrates significant robustness to complex non-Gaussian noise through the construction of an advanced regression model, the development of an adaptive free-parameter optimization scheme, and the implementation of a recursive filtering architecture incorporating entropy-based error correction. Comprehensive validation via numerical simulations and field experiments in offshore SINS/USBL integrated navigation scenarios demonstrates the superior robustness of the proposed method in complex underwater non-Gaussian noise environments.

关键词： minimum error entropy non-Gaussian noise robust filter SINS/USBL integration system

来源：评论

学校读者我要写书评

暂无评论

Adaptive Filtering Based on minimum error entropy Conjugate Gradient

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2024年第7期43卷 4662-4674页

作者： Li, Guoliang Sun, Qi Zhang, Ying Wang, Gang Yang, Xinyue Ouyang, Linqiang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Resources & Environm Chengdu 611731 Peoples R China

Based on the criterion of minimum error entropy, this paper proposes a novel conjugate gradient algorithm, called MEE-CG. This algorithm has robust performance under non-Gaussian interference. Theoretical analysis and experimental results demonstrate that the proposed algorithm displays more robust performance than the conventional conjugate gradient methods on the basis of the mean square error and the maximum correntropy criterion. Compared with the stochastic gradient minimum error entropy algorithm and the recursive minimum error entropy algorithm, the proposed algorithm provides a trade-off between computational complexity and convergence speed.

关键词： minimum error entropy Conjugate gradient Mean square error Algorithm

来源：评论

学校读者我要写书评

暂无评论

Parametric minimum error entropy Criterion: A Case Study in Blind Sensor Fusion and Regression Problems

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 5091-5106页

作者： Lopez, Carlos Alejandro Riba, Jaume Univ Politecn Catalunya UPC Dept Teoria Senyal & Comunicac Signal Proc & Commun Grp SPCOM Barcelona 08034 Spain

The purpose of this article is to present the Parametric minimum error entropy (PMEE) principle and to show a case study of the proposed criterion in a blind sensor fusion and regression problem. This case study consists on the estimation of a temporal series with a certain temporal invariance, which is measured from multiple independent sensors with unknown variances and unknown mutual correlations of the measurement errors. In this setting, we show that a particular case of the PMEE criterion is obtained from the Conditional Maximum Likelihood (CML) principle of the measurement model, leading to a semi-data-driven solution. Despite the fact that Information Theoretic Criteria (ITC) are inherently robust, they often result in difficult non-convex optimization problems. Our proposal is to address the non-convexity by means of a Majorization-Minimization (MM) based algorithm. We prove the conditions in which the resulting solution of the proposed algorithm reaches a stationary point of the original problem. In fact, the aforementioned global convergence of the proposed algorithm is possible thanks to a reformulation of the original cost function in terms of a variable constrained in the Grassmann manifold. As shown in this paper, the latter procedure is possible thanks to a homogeneity property of the PMEE cost function.

关键词： entropy Signal processing algorithms Cost function Pollution measurement Minimization Data integration Sensor fusion Robustness Probability density function Manifolds Non-convex optimization conditional maximum likelihood majorization-minimization minimum error entropy Grassmann manifold

来源：评论

学校读者我要写书评

暂无评论

Distributed minimum error entropy Kalman filter

引用

INFORMATION FUSION 2023年 91卷 556-565页

作者： Feng, Zhenyu Wang, Gang Peng, Bei He, Jiacheng Zhang, Kun Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Recently, the distributed Kalman filter (DKF) has been considered a major method for the applications of Wireless sensor networks (WSNs), for instance, the Internet of Things, and Swarm Intelligence, in which non-Gaussian noise influence is an urgent issue. In this paper, taking any sensor of WSNs as a fusion node, a dynamic Gain Matrix is constructed for its neighbors' information fusion. Then, the minimum error entropy (MEE) is furtherly introduced into the information fusion process, a modified DKF algorithm called the Distributed Fusion MEE Kalman Filter (DF-MEE-KF) is proposed for eliminating the non-Gaussian noise influence, which improved the estimation accuracy well. Moreover, considering many bad communication conditions of WSNs, such as Communication Denial Environments, and Underwater Acoustic Communication Environments, it is required that the higher estimation accuracy the better, and the lower communication cost the better. Therefore, the diffusion rule is applied for the nodes' information fusion by constructed fusion weights, thereby an extended DF-MEE-KF algorithm, the Diffusion MEE Kalman filter (Diff-MEE-KF), is obtained. Finally, the convergence of the proposed DF-MEE-KF and Diff-MEE-KF algorithms is proved. Numerical simulation examples also demonstrate that the DF-MEE-KF algorithm performs good estimation accuracy, and the Diff-MEE-KF algorithm achieves a lower communication cost under the same estimation accuracy, when in non-Gaussian noise-influenced WSNs' applications.

关键词： Wireless sensor networks Information fusion Distributed Kalman filter Diffusion strategy minimum error entropy

来源：评论

学校读者我要写书评

暂无评论

Augmented Complex minimum error entropy for Adaptive Frequency Estimation of Power System

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第3期69卷 1972-1976页

作者： Zhao, Haiquan Nefabas, Gebeyehu L. Wang, Zhuonan Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

The minimum error entropy (MEE) criterion has been receiving increasing attention over the minimum mean square error (MMSE) criterion in non-Gaussian noise distribution, because it accounts for all higher order moments. In this brief, a novel MEE algorithm was proposed by using information theoretical learning concepts and the widely linear (augmented) complex domain modelling approaches for enhanced power system frequency estimation. The proposed augmented complex minimum error entropy (ACMEE) utilizes the complex-valued voltage signal, modeled by the Clarke's alpha beta transformation, which used all second-order statistical information for processing of non-circular complex-valued voltage signals. Performance degradation of the MMSE criterion in impulsive noise environments can be overcome by MEE adaptation scheme due to the higher order moments imbedded in its cost function. Therefore, the proposed ACMEE algorithm is able to achieve robust frequency estimation for unbalanced conditions and under the interference of measurement noises. The effectiveness of the ACMEE frequency estimation technique is verified through simulation studies of synthetic signals and experimental data.

关键词： Frequency estimation entropy Estimation Voltage Power system stability Standards Adaptation models minimum error entropy robust frequency estimation widely linear (augmented) complex domain modelling

来源：评论

学校读者我要写书评

暂无评论

MIMO-OFDM channel estimation based on minimum error entropy criterion under non-Gaussian environment

引用

International Journal of Electronics Letters 2023年第3期11卷 267-280页

作者： Soltani, Arash Airamlozadeh, Mehdi Parchami, Jaber Department of Electrical Engineering Iran University of Science Technology Tehran Iran Department of Electrical Engineering Islamic Azad University Science and Research Branch Tehran Iran Department of Electrical Engineering Sadjad University of Technology Mashhad Iran

Using the orthogonal frequency division multiplexing (OFDM) technique, a multiple-input multiple-output (MIMO) system can provide high spectral efficiency and high data transmission over a fading channel. To achieve the best performance in a MIMO-OFDM system, high accuracy in channel estimation is a very important factor which leads to appropriate receiver design. Therefore, in the most channel estimation algorithms, the mean square error (MSE) is the main criterion for noise minimisation which is robust in the case of Gaussian noise. However, in telecommunication systems which do not have noise with Gaussian distribution, the MSE criterion is not appropriate. So as to tackle this problem, a robust adaptive filtering algorithm was proposed using minimum error entropy (MEE) criterion and improved least square (ILS) approach which by far is better than MMSE criterion to robust channel estimation. In this work, impulsive noise is used for non-Gaussian environment simulation. Furthermore, MEE is employed to attenuate the impulsive noise, and ILS is adopted to reduce LS estimation variance. Compared to the MSE-based algorithms, simulation results indicate that the proposed method outperforms the channel estimation in a non-Gaussian environment. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： adaptive filtering channel estimation improved least squares algorithm (ILS) MIMO-OFDM minimum error entropy

来源：评论

学校读者我要写书评

暂无评论

Fast rates of minimum error entropy with heavy-tailed noise

引用

JOURNAL OF APPROXIMATION THEORY 2022年 281卷

作者： Huang, Shouyou Hubei Normal Univ Dept Math & Stat Huangshi 435002 Peoples R China

In this paper, we investigate the performance of minimum error entropy (MEE) from a theoretical viewpoint. Owing to resistance of heavy-tailed noise or outliers, as an alternative to traditional robust empirical risk minimization schemes, MEE has drawn particular attention over the last decades and has been successfully used in machine learning. The purpose of this paper is to conduct refined learning theory analysis of MEE and establish its improved rates of convergence without the light-tailed noise. It shows that a new comparison theorem not only characterizes the regression calibration properties of MEE, but also refines the variance of analysis of learning theory.(c) 2022 Elsevier Inc. All rights reserved.

关键词： minimum error entropy Empirical risk Concentration inequality Convergence rate Moment condition Saturation effect

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：