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Estimator with Forgetting Factor of correntropy and Recursive Algorithm for Traffic Network Prediction

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Estimator with Forgetting Factor of Correntropy and Recursiv...

A NEW correntropy BASED TDE METHOD UNDER α-STABLE DISTRIBUTION NOISE ENVIRONMENT

第25届中国控制与决策会议

作者： Wentao Ma Hua Qu Jihong Zhao School of Electronic and Information Engineering Xi'an Jiaotong University School of Telecommunication and information Engineering Xi'an University of Posts and Telecommunications

ISBN: (纸本)9781467355339

correntropy as a new similarity function with its excellent property has been used for many case. It’s estimator as a computing tool of correntropy in sense of mean value, this character has some defect for tracking time-varying parameters in some special case. Thus the estimator with forgetting factor of correntropy （correntropy-FF） is researched by us first in this paper. correntropy-FF is as an estimator of correntropy in sense of weight average. It can promise some important factor for effect of estimate results. A kind of recursive learning algorithm is deduced based on the correntropy-FF as a cost function. In general, the current result is impacted by nearing it in the time varying system, so kernel function in correntropy-FF playing this role as correntropy. The simulation results on traffic network prediction indicate that the effective performance and available of the proposed algorithm.

关键词： correntropy Forgetting factor Recursive learning Traffic network prediction

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Journal of Electronics(China) 2011年第3期28卷 284-288页

作者： Song Aimin Tong Zhijian Qiu Tianshuang Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian 115024 China School of Science Dalian Jiaotong University Dalian 116028 China

This paper presents a robust time delay estimation algorithm for the α-Stable noise based on correntropy. Many time delay estimation algorithms derived for impulsive stable noise are based on the theory of Fractional Lower Order Statistics (FLOS). Unlike previously introduced FLOS-type algorithms, the new algorithm is proposed to estimate the time delay by maximizing the generalized correlation function of two observed signals needing neither prior information nor estimation of the numerical value of the stable noise's characteristic exponent. An interval for kernel selection is found for a wide range of characteristic exponent values of α-Stable distribution. Simulations show the proposed algorithm offers superior performance over the existing covariation time delay estimation, least mean p-norm time delay estimation and achieves slightly improved performance than fractional lower order covariance time delay estimation at lower signal to noise ratio when the noise is highly impulsive.

关键词： α-Stable Distribution correntropy Generalized correntropy Function (GCF)

Feature Extraction by correntropy Based Average Neighborhood Margin Maximization

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Feature Extraction by Correntropy Based Average Neighborhood...

Training RBFNN with reglarized correntropy criterion and its application to foreign exchange rate forecasting

第26届中国控制与决策会议

作者： Lin-Na Ma Hong-Jie Xing Shun-Yan Hou Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer ScienceHebei University College of Electronics and Information Engineering Hebei University

ISBN: (纸本)9781479937097

Average neighborhood margin maximization（ANMM） is a feature extraction method to make homogeneous points collect as near as possible and heterogeneous points disperse as far away as possible. To enhance the anti-noise ability of ANMM, correntropy based average neighborhood margin maximization（CANMM） is proposed in this paper. This method utilizes correntropy to substitute the Euclidean distance for measuring the similarity between the given data, and uses the maximum correntropy criterion to replace the maximum distance criterion, which makes CANMM more robust. The experimental results on three benchmark face databases validate the effectiveness of the proposed method.

关键词： Feature Extraction Half-quadratic optimization correntropy ANMM

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Training RBFNN with reglarized correntropy criterion and its...

Combining supervised classifiers with unlabeled data

第26届中国控制与决策会议

作者： Yan-Jun Liu Hong-Jie Xing Institute of Japanese Studies Hebei University College of Mathematics and Computer Science Hebei University

In the paper, a regularized correntropy criterion（RCC） for radial basis function neural network（RBFNN） is proposed. In RCC, the Gaussian kernel function is used to replace the Eculidean norm of the sum-squared-error（SSE）criterion. Replacing SSE by RCC can improve the anti-noise ability of RBFNN. Moreover, the optimal weights and the optimal bias terms can be iteratively obtained by the half-quadratic optimization technique. The effectiveness of the proposed method is validated on the foreign exchange rate time series. In comparison with the RBFNN trained with the SSE criterion, the proposed method demonstrates better generalization ability.

关键词： RBFNN correntropy Exchange rates Neural networks

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Journal of Central South University 2016年第5期23卷 1176-1182页

作者：刘雪艳张雪英李凤莲黄丽霞 College of Information Engineering Taiyuan University of Technology

Ensemble learning is a wildly concerned *** ensemble techniques are always adopted to seek better results with labeled data and base *** fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled ***,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy *** prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the *** proposed approach is further enriched when small labeled data are *** proposed algorithms are evaluated on several UCI benchmark data *** of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

关键词： correntropy unlabeled data regularization framework ensemble learning

Robust Object Tracking via Information Theoretic Measures

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International Journal of Automation and computing 2020年第5期17卷 652-666页

作者： Wei-Ning Wang Qi Li Liang Wang Center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Artificial Intelligence Research Chinese Academy of SciencesQingdao 266300China

Object tracking is a very important topic in the field of computer *** sophisticated appearance models have been *** them,the trackers based on holistic appearance information provide a compact notion of the tracked object and thus are robust to appearance variations under a small amount of ***,in practice,the tracked objects are often corrupted by complex noises(e.g.,partial occlusions,illumination variations)so that the original appearance-based trackers become less *** paper presents a correntropy-based robust holistic tracking algorithm to deal with various ***,a half-quadratic algorithm is carefully employed to minimize the correntropy-based objective *** on the proposed information theoretic algorithm,we design a simple and effective template update scheme for object *** results on publicly available videos demonstrate that the proposed tracker outperforms other popular tracking algorithms.

关键词： Object tracking information theoretic measures correntropy template update robust to complex noises

Kernel Entropy Based Extended Kalman Filter for GPS Navigation Processing

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Computers, Materials & Continua 2021年第7期68卷 857-876页

作者： Dah-Jing Jwo Jui-Tao Lee Department of Communications Navigation and Control EngineeringTaiwan Ocean UniversityKeelung202301Taiwan

This paper investigates the kernel entropy based extended Kalman filter(EKF)as the navigation processor for the Global Navigation Satellite Systems(GNSS),such as the Global Positioning System(GPS).The algorithm is effective for dealing with non-Gaussian errors or heavy-tailed(or impulsive)interference errors,such as the *** kernel minimum error entropy(MEE)and maximum correntropy criterion(MCC)based filtering for satellite navigation system is involved for dealing with non-Gaussian errors or heavy-tailed interference errors or outliers of the *** standard EKF method is derived based on minimization of mean square error(MSE)and is optimal only under Gaussian assumption in case the system models are precisely *** GPS navigation algorithm based on kernel entropy related principles,including the MEE criterion and the MCC will be performed,which is utilized not only for the time-varying adaptation but the outlier type of interference *** kernel entropy based design is a new approach using information from higher-order signal *** information theoretic learning(ITL),the entropy principle based measure uses information from higher-order signal statistics and captures more statistical information as compared to *** improve the performance under non-Gaussian environments,the proposed filter which adopts the MEE/MCC as the optimization criterion instead of using the minimum mean square error(MMSE)is utilized for mitigation of the heavy-tailed type of multipath *** assessment will be carried out to show the effectiveness of the proposed approach for positioning improvement in GPS navigation processing.

关键词： GPS satellite navigation extended Kalman filter entropy correntropy multipath non-Gaussian