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Local Information-Driven Intuitionistic Fuzzy C-Means Algorithm Integrating Total Bregman Divergence and kernel Metric for Noisy Image Segmentation

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2022年第3期42卷 1522-1572页

作者： Wu, Chengmao Huang, Congcong Zhang, Jiajia Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

To improve the segmentation performance and anti-noise robustness of existing weighted kernel intuitionistic fuzzy clustering, a robust kernelized total Bregman divergence-based fuzzy local information clustering motivated by intuitionistic fuzzy information is proposed. In this algorithm, a kernelized total Bregman divergence is extended by a polynomial kernel function, and the corresponding intuitionistic kernelized total Bregman divergence is put forward to measure the difference between intuitionistic fuzzy sets. Then, the weighted local information is introduced into the objective function of intuitionistic fuzzy clustering, and the similarity between the current pixel and its neighborhood pixels is constructed to better describe the influence of neighborhood pixels on the current pixel. Finally, the square root of the deviation between the current pixel and the mean value of its neighboring pixels is used to adjust the local spatial information to improve the robustness to noise or outliers and further enhance the anti-noise ability of the algorithm. Experimental results show that the proposed algorithm has better clustering performance and stronger anti-noise robustness than existing state-of-the-art fuzzy clustering-related algorithms.

关键词： Intuitionistic fuzzy clustering Anti-noise robustness Total Bregman divergence Local similarity constraint polynomial kernel function

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Distribution Network Load Forecasting Based on Data Analysis Model 9

Distribution Network Load Forecasting Based on Data Analysis...

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9th International Forum on Electrical Engineering and Automation (IFEEA)

作者： Zhang, Mingze Hua, Shenghui Fei, Fei Wang, Jun Zhao, Wanjian State Grid Shanghai Elect Power Co Econ & Tech Res Inst Shanghai 200000 Peoples R China Shanghai Songjiang Elect Power Corp State Grid Shanghai 200000 Peoples R China

ISBN: (纸本)9781665464215

Short-term load forecasting is one of the important bases of power grid planning, so how to improve the accuracy of power load forecasting is the top priority of short-term load forecasting. In order to effectively improve the accuracy of power load forecasting, considering that there are many factors affecting power load, the combined model used in this paper will face the problems of too long training time and too complex a network structure of the model. This paper introduces the KPCA model, which can not guarantee the learning ability and generalization ability of a single kernel function at the same time. The MKPCA model based on the mixture of Gaussian radial kernel function and polynomial kernel function is proposed. The structure and specific steps of the RBFNN neural network are designed and the power load forecasting method based on the MKPCA-RBFNN model is proposed. Compared with the KPCA-RBFNN model through simulation experiments, it is verified that the prediction accuracy of the proposed model is superior to other traditional models, and it has practical promotion value and plays a role in scientific data analysis in the construction of distribution networks.

关键词： Data Analysis Model KPCA model distribution networks polynomial kernel function

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A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentation

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2021年 136卷 281-305页

作者： Wu, Chengmao Zhang, Xue Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Peoples R China

kernel weighted fuzzy local information c-means clustering (KWFLICM) is of great significance in the field of fuzzy clustering. Nevertheless, it is sensitive to high noise or outliers. Aiming at the shortcoming of KWFLICM algorithm, this paper proposes a novel fuzzy local information c-means clustering algorithm utilizing total Bregman divergence driven by polynomial kernel function (TKWFLICM). Firstly, total Bregman divergence (TBD) is kernelized by global polynomial kernel function to solve the problem of kernel-based distance metric without rotation-invariance. Secondly, TBD with polynomial kernel metric is modified by local neighborhood information of current pixel to further enhance the ability to suppress the noise in image. In the end, the modified TBD with polynomial kernel is introduced into the objective function of KWFLICM, and then a novel robust fuzzy clustering with high robustness is obtained. Experimental results show that the proposed algorithm performs better than existing state-of-the-art KWFLICM algorithm, and the kernelized TBD can also be utilized in fuzzy clustering-related algorithms, which will enhance the anti-noise robustness of these algorithms. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Image segmentation Fuzzy clustering Total Bregman divergence polynomial kernel function

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Total Bregman divergence-driven possibilistic fuzzy clustering with kernel metric and local information for grayscale image segmentation

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PATTERN RECOGNITION 2022年 128卷

作者： Wu, Chengmao Zhang, Xue Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

kernel possibilistic fuzzy C-means with local information (KWPFLICM) has important research significance of image segmentation, but it is very sensitive to high noise or outliers. To enhance the segmentation per-formance of the algorithm, this paper proposes a kernelized total Bregman divergence-driven possibilistic fuzzy clustering with local information (TKWPFLICM). Firstly, a polynomial kernel function is introduced to kernelize total Bregman divergence (TBD), and local neighborhood information of the pixel is used to modify it, which overcomes the shortcomings of Bregman divergence (BD) with rotation variability;Secondly, the modified kernelized TBD and possibilistic typicality are combined to further enhance the anti-noise ability of the algorithm;Finally, the modified kernelized TBD is introduced into the objective function of KWPFLICM algorithm, then a novel robust fuzzy clustering algorithm is derived by optimiza-tion theory. Experimental results show that compared with existing fuzzy clustering-related algorithms, the average SA improvement on TKWPFLICM algorithm is in the range of 0.791% to 33.237%. Therefore, TKWPFLICM algorithm has better anti-noise robustness and segmentation accuracy. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： Image segmentation Fuzzy clustering Total Bregman divergence polynomial kernel function Possibilistic typicality

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Synchronous Optimization Schemes for Dynamic Systems Through the kernel-Based Nonlinear Observer Canonical Form

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Li, Jimei Ding, Feng Jiangnan Univ Sch Internet Things Engn Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

Although some dynamic behaviors are commonly modeled as the linear or bilinear systems, recent studies have consistently noted the existence of significant nonlinearities in these behaviors. Here, this work attempts to devise effective models and optimization approaches to represent and analyze the nonlinear phenomena ground on the online measured data. A kernel-based nonlinear observer canonical form is put forward by exploiting the fitting superiorities of kernel functions, which comprises the classical linear and bilinear state-space models as the special cases. The utilization of polynomial properties reduces the difficulty of identification while preserving the capacity of state-space models to fit nonlinear characteristics. Aiming to fulfilling simultaneous state and parameter estimation, a nonlinear state observer-based separable hierarchical forgetting gradient (NSO-SHFG) algorithm is devised in accordance with the extended Kalman filtering and the decomposition technique. The convergence analysis is established to verify the theoretical results, and the feasibility of the NSO-SHFG algorithm is validated by simulations.

关键词： Observers Optimization kernel Kalman filters Mathematical models Estimation Covariance matrices Extended Kalman filtering (EKF) hierarchical identification principle nonlinear state observer (NSO) parameter estimation polynomial kernel function

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Research on Prediction of Protein Sub-cellular Location Based on KLDA with Combined kernel function 4

Research on Prediction of Protein Sub-cellular Location Base...

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4th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Nie, Bing Wang, Shunfang Xu, Dongshu Yunnan Univ Sch Informat Sci & Engn Kunming 650504 Peoples R China

ISBN: (纸本)9781467381734

To improve the accuracy of prediction of subcellular location, a new method using kernel linear discriminant analysis with combinational kernel function which is made up of the Gauss kernel function and the polynomial kernel function is used to the predict the sub-cellular location. In order to confirm the reliability of the research, the data used in this paper are from the standard data set included in Swiss-Prot database and the values of parameters for combined kernel function are determined reasonably. The results indicate that the proposed method with combined kernel function is more efficient than the kernel linear discriminant analysis algorithm with traditional kernel functions in the prediction of sub-cellular location.

关键词： combined kernel function kernel linear discriminant analysis Gauss kernel function polynomial kernel function protein sub-cellular localization

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Research on Prediction of Protein Sub-cellular Location Based on KLDA with Combined kernel function

Research on Prediction of Protein Sub-cellular Location Base...

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International Conference on Computer Science and Network Technology

作者： Bing Nie Shunfang Wang Dongshu Xu School of Information Science and Engineering Yunnan University Kunming 650504 China

ISBN: (纸本)9781467381741

关键词： combined kernel function kernel linear discriminant analysis Gauss kernel function polynomial kernel function protein sub-cellular localization kernel functionS Seeds kernel Linear discriminant analysis

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Parameter Optimization Using GA in SVM to Predict Damage Level of Non-Reshaped Berm Breakwater

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Journal of Ocean and Climate 2014年第2期5卷 79-88页

作者： N. Harish N. Lokesha S. Mandal Subba Rao S.G. Patil Assistant Professor CET Jain University Jakksandra Post Ramanagara District 562112 Karnataka India Reseach Scholar Department of Ocean Engineering Indian Institute of Technology Madras Chennai Tamil Nadu 600036 INDIA Chief Scientist Ocean Engineering Division National Institute of Oceanography Dona Paula 403004 Goa India Professor Department of Applied Mechanics and Hydraulics National Institute of Technology Surathkal Karnataka 575025 India Professor Department of Built and Natural Environment Caledonian College of Engineering PO Box: 2322 CPO Seeb PC 111 Sultanate of Oman

In the present study, Support Vector Machines (SVM) and hybrid of Genetic Algorithm (GA) with SVM models are developed to predict the damage level of non-reshaped berm breakwaters. Optimal kernel parameters of SVM are determined by using GA algorithm. The models are trained and tested on the data set obtained from the experiments which were carried out at Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, India. The results of SVM and GA-SVM models are compared in terms of statistical measures like correlation coefficient, root mean square error and scatter index. The results on SVM and GA-SVM models reveals that the performance of GA-SVM is better compared to SVM models in predicting the damage level of non-reshaped berm breakwater.

关键词： Hybrid model polynomial kernel function Statistical parameters

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Research on the selection of kernel function in SVM based facial expression recognition

Research on the selection of kernel function in SVM based fa...

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8th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wang, Fuguang He, Ketai Liu, Ying Li, Li Hu, Xiaoguang Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China BeiHang Univ Sch Automat Sci Elect Engn Beijing Peoples R China

ISBN: (纸本)9781467363228;9781467363204

Support vector machine(SVM) means that structural risk minimization principle is used to substitute Empirical risk minimization principle. SVM has shown the excellent performance in pattern recognition. The kernel function is the core of SVM, with which SVM can help to resolve many kinds of non-linear classification problems. Different kernel models and parameters have different result in the performance of the facial expression recognition system. The authors analyze the capability of polynomial kernel function and RBF kernel function in the facial expression recognition using the JAFFE expressions library. The work is valuable in the choise of kernel and its parameters in practice.

关键词： Support vector machine polynomial kernel function RBF kernal function Facial expression recognition

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32nd Chinese Control Conference (CCC)

作者： Li Junli Yin Gengyun Mao Jianlin Duan Shaomi Kunming Univ Sci & Technol Coll Informat Engn & Automat Kunming 650051 Peoples R China Yunnan Acad Tobacco Sci Kunming 650106 Peoples R China

ISBN: (纸本)9781479900305

The type and content of aroma components are the main factors affecting the tobacco style. For tobacco production enterprises, the style analysis is very important to compare their own products with the competitors. Principal component analysis method based on kernel function is a nonlinear multivariate statistical analysis method with favorable effect in dimensionality reduction. In this paper, the high-dimensional data up to 63 index of tobacco aroma compositions were mapped into two-dimensional feature space by kernel principal component analysis, and the information amount of feature data is not less than 95%. Inter-class dispersion was introduced to evaluate the aroma components similarity of nine brands products. The results showed that the evaluation of nine brands cigarette based on the proposed method was consistent with the Chinese cigarette style feature classification and brand market segmentation. Such method can provide effective theoretical support for the traditional classification method of cigarette-style feature.

关键词： aroma components kernel principal component analysis polynomial kernel function Inter-class dispersion

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