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2002海峡两岸三地无线科技研讨会

作者： DAI Wen-liang, LI Zheng-fan, LI Fu-hua Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai, 200030, China

Instead of using the total charge Green抯 function method directly to compute the two-dimensional parameters of interconnects in multi-dielectric media, the Appel抯 hierarchical algorithm is adopted to

关键词： boundary element method hierarchical algorithm total charge Green抯 function method

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A Density-based hierarchical Clustering algorithm for Highly Overlapped Distributions with Noisy Points

A Density-based Hierarchical Clustering Algorithm for Highly...

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13th International Conference of the Catalan-Association-for-Artificial-Intelligence (CCIA)

作者： Pascual, Damaris Pla, Filiberto Salvador Sanchez, J. Univ Jaume 1 Inst New Imaging Technol Dept Llentguages & Sistemas Informat Av Vicent Sos Baynat S-N Castellon de La Plana 12071 Spain

ISBN: (纸本)9781607506430;9781607506423

In this work, we present a new two-stage technique to find clusters of different shapes, densities and sizes in the presence of overlapped clusters and noise. Firstly, a density-based clustering approach is developed using a density function estimated by the EM algorithm and in the second stage, a hierarchical strategy is used to merge clusters according to a dissimilarity measure here introduced in order to assess the overlap and proximity of the clusters. Several synthetic and real world data sets are used to evaluate the effectiveness and the efficiency of the new algorithm, indicating that it obtains satisfactory clustering results.

关键词： Clustering Density-based algorithm hierarchical algorithm Overlapping Noise

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Modified hierarchical Clustering algorithm for Time Series Data 3

Modified Hierarchical Clustering Algorithm for Time Series D...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Rani, Sangeeta Amity Univ Dept Comp Sci & Engn Noida Uttar Pradesh India

ISBN: (纸本)9789380544199

Time-Series clustering is used to attain deep knowledge of the mechanism that generate the time-series and speculate the prospective values of the given time-series. Time Series clustering is shape- level if it is carried out on the many individual time-series or structure-level if it works on single long length time-series. Depending on whether Time-series clustering is working directly on unprocessed data (frequency or time domain), or indirectly with the features extracted or model built from the unprocessed data, it is categorized into three groups. The proposed work comes under the raw data based approach. In this work, DTW is utilized as a distance/similarity count in the hierarchical clustering algorithm with inter/intra-cluster-distance-based-swap. The performance of the proposed work is evaluated by using Clustering Validity indices

关键词： Dynamic Time Warping hierarchical algorithm CVAP Validity Indices

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A hierarchical Classification algorithm for Evaluating Energy Consumption Behaviors

A Hierarchical Classification Algorithm for Evaluating Energ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Bu, Li Zhao, Dongbin Liu, Yu Guan, Qiang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9781479914845

Researches on office building energy consumption have been hot in these years, but few researchers consider the classification of office energy consumption performance which can evaluate user behaviors in order to offer a clear analysis of energy consumption and improve their energy saving consciousness. In this paper, we propose a novel hierarchical classification algorithm for evaluating energy consumption behaviors at a real energy management system, which combines fuzzy c-means clustering with GA (genetic algorithm)-based SVM (support vector machine) to fully utilize collected samples. The experiment results with real energy consumption data show that the proposed algorithm works well to distinguish the abnormal behaviors and classify energy consumption behaviors accurately on normal offices.

关键词： building energy consumption classification hierarchical algorithm support vector machine

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A Greedy Pursuit hierarchical Iteration algorithm for Multi-Input Systems with Colored Noise and Unknown Time-Delays

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algorithmS 2023年第8期16卷 374-374页

作者： Du, Ruijuan Tao, Taiyang Nanyang Inst Technol Sch Intelligent Mfg Nanyang 473004 Peoples R China

This paper focuses on the joint estimation of parameters and time delays for multi-input systems that contain unknown input delays and colored noise. A greedy pursuit hierarchical iteration algorithm is proposed, which can reduce the estimation cost. Firstly, an over-parameterized approach is employed to construct a sparse system model of multi-input systems even in the absence of prior knowledge of time delays. Secondly, the hierarchical principle is applied to replace the unknown true noise items with their estimation values, and a greedy pursuit search based on compressed sensing is employed to find key parameters using limited sampled data. The greedy pursuit search can effectively reduce the scale of the system model and improve the identification efficiency. Then, the parameters and time delays can be estimated simultaneously while considering the known orders and found locations of key parameters by utilizing iterative methods with limited sampled data. Finally, some simulations are provided to illustrate the effectiveness of the presented algorithm in this paper.

关键词： large-scale systems time delays parameter estimation system identification hierarchical algorithm

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hierarchical algorithms of Quasi-Linear ARX Neural Networks for Identification of Nonlinear Systems

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ENGINEERING LETTERS 2017年第3期25卷 321-328页

作者： Abu Jami'in, Mohammad Yuyun Julianto, Eko Politekn Perkapalan Negeri Surabaya Dept Automat Engn Surabaya Jawa Timur Indonesia STMKI Handayani Dept Comp Engn Makassar Sulawesi Selata Indonesia Politekn Perkapalan Negeri Surabaya Dept Piping Engn Surabaya Jawa Timur Indonesia

A quasi-linear ARX neural network model (QARXNN) is a nonlinear model built using neural networks (NN). It has a linear-ARX structure where NN is an embedded system to give the parameters for the regression vector. There are two contributions in this paper, 1) hierarchical algorithms is proposed for the training of QARXNN model, 2) an adaptive learning is implemented to update learning rate in NN training to ensure global minima. First, the system is estimated by using LSE algorithm. Second, nonlinear sub-model performed using NN is trained to refine error of LSE algorithm. The linear parameters obtained from LSE algorithm is set as bias vector for the output nodes of NN. Global minima point is reached by adjusting the learning rate based on Lyapunov stability theory to ensure convergence of error. The proposed algorithm is used for the identification and prediction of nonlinear dynamic systems. Finally, the experiments and numerical simulations reveal that the proposed method gives satisfactory results.

关键词： System Identification quasi linear-ARX neural network hierarchical algorithm convergence of error nonlinear system

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A novel relaxed hierarchical equivalent source algorithm (RHESA) for electromagnetic scattering analysis of dielectric objects

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2016年第12期30卷 1631-1642页

作者： Fu, Xin Jiang, Li-Jun Ewe, Hong Tat Univ Hong Kong Dept Elect & Elect Engn Hong Kong Hong Kong Peoples R China Univ Tunku Abdul Rahman Dept Internet Engn & Comp Sci Petaling Jaya Selangor Malaysia

A novel relaxed hierarchical algorithm based on the surface equivalence principle for the volume integral equations (VIEs) is proposed in this work. The equivalent sources residing on relaxed spherical equivalence surfaces are established by exact integral formulations. The equivalence surfaces are exploited hierarchically to accelerate matrix-vector product in iterative solutions for the VIEs. The computation time and memory cost complexity of the proposed algorithm for dielectric scatters scale as O (N-4/3) and O (N), respectively. Numerical examples validate the accuracy and capability of the proposed algorithm.

关键词： Equivalent source method hierarchical algorithm volume integral equation electromagnetic scattering

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Cooperative control for platoon generation of vehicle-to-vehicle networks: a hierarchical nonlinear MPC algorithm

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NONLINEAR DYNAMICS 2022年第4期108卷 3561-3578页

作者： Liu, Wen-Jin Ding, Hua-Feng Ge, Ming-Feng Yao, Xiang-Yu China Univ Geosci Wuhan Sch Mech Engn & Elect Informat Wuhan 430074 Peoples R China

This paper develops a hierarchical nonlinear model predictive control (MPC) algorithm for the cooperative control of vehicle-to-vehicle (V2V) networks. In the hierarchical algorithm, an estimator is proposed as a single layer, namely, the estimated layer for generating sub-targets with local physical constraints. Then, based on an augmented simplified vehicle model, a nonlinear MPC technique is newly presented in the local layer for planning the desired trajectory with its optimization solved by a continuation/generalized minimal residual (C/GMRES) method. Note that, by considering the error components between the planned trajectory and the measured positions, a controller with a two-loop structure is adopted to improve tracking performances. Therefore, the control objective is obtained, namely, the vehicle platoon in the desired path is generated and the collisions among vehicles are avoided. The simulation results with comparisons are finally given to illustrate the effectiveness of the algorithm.

关键词： Autonomous vehicle hierarchical algorithm Nonlinear model predictive control (NMPC) Vehicle platoon generation Real-time optimization

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Parallel hierarchical Pre-Gauss-Seidel Value Iteration algorithm

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INTERNATIONAL JOURNAL OF DECISION SUPPORT SYSTEM TECHNOLOGY 2018年第2期10卷 1-22页

作者： Chafik, Sanaa Larach, Abdelhadi Daoui, Cherki Sultan Moulay Slimane Univ Beni Mellal Morocco Sultan Moulay Slimane Univ Dept Math Beni Mellal Morocco

The standard Value Iteration (VI) algorithm, referred to as Value Iteration Pre-Jacobi (PJ-VI) algorithm, is the simplest Value Iteration scheme, and the well-known algorithm for solving Markov Decision Processes (MDPs). In the literature, several versions of VI algorithm were developed in order to reduce the number of iterations: the VI Jacobi (VI-J) algorithm, the Value Iteration Pre-Gauss-Seidel (VI-PGS) algorithm and the VI Gauss-Seidel (VI-GS) algorithm. In this article, the authors combine the advantages of VI Pre Gauss-Seidel algorithm, the decomposition technique and the parallelism in order to propose a new Parallel hierarchical VI Pre-Gauss-Seidel algorithm. Experimental results show that their approach performs better than the traditional VI schemes in the case where the global problem can be decomposed into smaller problems.

关键词： Decomposition hierarchical algorithm Markov Decision Process Markov Model Parallel hierarchical algorithm Parallelism Pre-Gauss-Seidel Value Iteration

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Interpreting the Basis Path Set in Neural Networks

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Journal of Systems Science & Complexity 2021年第6期34卷 2155-2167页

作者： ZHU Juanping MENG Qi CHEN Wei MA Zhiming Department of Mathematics Yunnan UniversityYunnan 650500China Microsoft Research Asia Beijing 100190China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China

The G-SGD algorithm significantly outperforms the conventional SGD algorithm in ReLU neural networks by adopting the basis path ***,how the inner mechanism of basis paths works remains mysterious,and the G-SGD algorithm that helps to find a basis path set is *** paper employs graph theory to investigate structure properties of basis paths in a more general and complicated neural network with unbalanced layers and *** hierarchical algorithm HBPS is proposed to find a basis path set,by decomposing the complicated network into several independent and parallel *** paper theoretically extends the study of basis paths and provides one methodology to find the basis path set in a more general neural network.

关键词： Basis path hierarchical algorithm independent path neural network substructure path

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