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Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing 认领 引用 被引量：2
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作者 Tao Li Qi Qian +2 位作者 Yongjun Ren Yongzhen Ren Jinyue Xia 《Computers, Materials & Continua》 SCIE EI 2021年第1期779-791,共13页
The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively... The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy. 展开更多
关键词 IoT kernel method privacy-preserving personalized recommendation random perturbation
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Adaptive multi-step piecewise interpolation reproducing kernel method for solving the nonlinear time-fractional partial differential equation arising from financial economics 认领 引用 被引量：2
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作者 杜明婧 孙宝军 凯歌 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第3期53-57,共5页
This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)metho... This paper is aimed at solving the nonlinear time-fractional partial differential equation with two small parameters arising from option pricing model in financial economics.The traditional reproducing kernel(RK)method which deals with this problem is very troublesome.This paper proposes a new method by adaptive multi-step piecewise interpolation reproducing kernel(AMPIRK)method for the first time.This method has three obvious advantages which are as follows.Firstly,the piecewise number is reduced.Secondly,the calculation accuracy is improved.Finally,the waste time caused by too many fragments is avoided.Then four numerical examples show that this new method has a higher precision and it is a more timesaving numerical method than the others.The research in this paper provides a powerful mathematical tool for solving time-fractional option pricing model which will play an important role in financial economics. 展开更多
关键词 time-fractional partial differential equation adaptive multi-step reproducing kernel method method numerical solution
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Modified possibilistic clustering model based on kernel methods 认领 引用
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作者 武小红 周建江 《Journal of Shanghai University(English Edition)》 2008年第2期136-140,共5页
A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means ... A novel model of fuzzy clustering using kernel methods is proposed. This model is called kernel modified possibilistic c-means （KMPCM） model. The proposed model is an extension of the modified possibilistic c-means （MPCM） algorithm by using kernel methods. Different from MPCM and fuzzy c-means （FCM） model which are based on Euclidean distance, the proposed model is based on kernel-induced distance. Furthermore, with kernel methods the input data can be mapped implicitly into a high-dimensional feature space where the nonlinear pattern now appears linear. It is unnecessary to do calculation in the high-dimensional feature space because the kernel function can do it. Numerical experiments show that KMPCM outperforms FCM and MPCM. 展开更多
关键词 fuzzy clustering kernel methods possibilistic c-means （PCM） kernel modified possibilistic c-means （KMPCM）.
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Adaptive Single Piecewise Interpolation Reproducing Kernel Method for Solving Fractional Partial Differential Equation 认领 引用
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作者 DU Mingjing 《Journal of Donghua University(English Edition)》 CAS 2022年第5期454-460,共7页
It is well-known that using the traditional reproducing kernel method(TRKM) for solving the fractional partial differential equation(FPDE) is very intractable. In this study, the adaptive single piecewise interpolatio... It is well-known that using the traditional reproducing kernel method(TRKM) for solving the fractional partial differential equation(FPDE) is very intractable. In this study, the adaptive single piecewise interpolation reproducing kernel method(ASPIRKM) is determined to solve the FPDE. This improved method not only improves the calculation accuracy, but also reduces the waste of time. Two numerical examples show that the ASPIRKM is a more time-saving numerical method than the TRKM. 展开更多
关键词 fractional partial differential equation(FPDE) reproducing kernel method(RKM) single piecewise numerical solution
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Laguerre reproducing kernel method in Hilbert spaces for unsteady stagnation point ow over a stretching/shrinking sheet 认领 引用
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作者 M.R.Foroutan A.S.Gholizadeh +1 位作者 Sh.Najafzadeh R.H.Haghi 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2021年第3期354-369,共16页
This paper investigates the nonlinear boundary value problem resulting from the exact reduction of the Navier-Stokes equations for unsteady magnetohydrodynamic boundary layer flow over the stretching/shrinking permeab... This paper investigates the nonlinear boundary value problem resulting from the exact reduction of the Navier-Stokes equations for unsteady magnetohydrodynamic boundary layer flow over the stretching/shrinking permeable sheet submerged in a moving fluid.To solve this equation,a numerical method is proposed based on a Laguerre functions with reproducing kernel Hilbert space method.Using the operational matrices of derivative,we reduced the problem to a set of algebraic equations.We also compare this work with some other numerical results and present a solution that proves to be highly accurate. 展开更多
关键词 nonlinear boundary value problem Laguerre reproducing kernel method operational matrix of derivative existence and nonexistence of solutions approximate solution
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Alternating minimization for data-driven computational elasticity from experimental data: kernel method for learning constitutive manifold 认领 引用
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作者 Yoshihiro Kanno 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2021年第5期260-265,共6页
Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected ... Data-driven computing in elasticity attempts to directly use experimental data on material,without constructing an empirical model of the constitutive relation,to predict an equilibrium state of a structure subjected to a specified external load.Provided that a data set comprising stress-strain pairs of material is available,a data-driven method using the kernel method and the regularized least-squares was developed to extract a manifold on which the points in the data set approximately lie(Kanno 2021,Jpn.J.Ind.Appl.Math.).From the perspective of physical experiments,stress field cannot be directly measured,while displacement and force fields are measurable.In this study,we extend the previous kernel method to the situation that pairs of displacement and force,instead of pairs of stress and strain,are available as an input data set.A new regularized least-squares problem is formulated in this problem setting,and an alternating minimization algorithm is proposed to solve the problem. 展开更多
关键词 Alternating minimization Regularized least-squares Kernel method Manifold learning Data-driven computing
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Kernel method-based fuzzy clustering algorithm 认领 引用 被引量：2
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作者 WuZhongdong GaoXinbo +1 位作者 XieWeixin YuJianping 《Journal of Systems Engineering and Electronics》 2005年第1期160-166,共7页
The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, d... The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis. 展开更多
关键词 fuzzy clustering analysis kernel method fuzzy C-means clustering.
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Fault Pattern Recognition based on Kernel Method and Fuzzy C-means 认领 引用
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作者 SUN Yebei ZHAO Rongzhen TANG Xiaobin 《International Journal of Plant Engineering and Management》 2016年第4期231-240,共10页
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c... A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery. 展开更多
关键词 Kernel method fuzzy C-means FCM pattern recognition clustering
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AHermitian C2 Differential Reproducing Kernel Interpolation Meshless Method for the 3D Microstructure-Dependent Static Flexural Analysis of Simply Supported and Functionally Graded Microplates 认领 引用 被引量：2
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作者 Chih-Ping Wu Ruei-Syuan Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第10期917-949,共33页
This work develops a Hermitian C2 differential reproducing kernel interpolation meshless(DRKIM)method within the consistent couple stress theory(CCST)framework to study the three-dimensional(3D)microstructuredepend... This work develops a Hermitian C2 differential reproducing kernel interpolation meshless(DRKIM)method within the consistent couple stress theory(CCST)framework to study the three-dimensional(3D)microstructuredependent static flexural behavior of a functionally graded(FG)microplate subjected to mechanical loads and placed under full simple supports.In the formulation,we select the transverse stress and displacement components and their first-and second-order derivatives as primary variables.Then,we set up the differential reproducing conditions(DRCs)to obtain the shape functions of the Hermitian C2 differential reproducing kernel(DRK)interpolant’s derivatives without using direct differentiation.The interpolant’s shape function is combined with a primitive function that possesses Kronecker delta properties and an enrichment function that constituents DRCs.As a result,the primary variables and their first-and second-order derivatives satisfy the nodal interpolation properties.Subsequently,incorporating ourHermitianC2DRKinterpolant intothe strong formof the3DCCST,we develop a DRKIM method to analyze the FG microplate’s 3D microstructure-dependent static flexural behavior.The Hermitian C2 DRKIM method is confirmed to be accurate and fast in its convergence rate by comparing the solutions it produces with the relevant 3D solutions available in the literature.Finally,the impact of essential factors on the transverse stresses,in-plane stresses,displacements,and couple stresses that are induced in the loaded microplate is examined.These factors include the length-to-thickness ratio,the material length-scale parameter,and the inhomogeneity index,which appear to be significant. 展开更多
关键词 Consistent/modified couple stress theory differential reproducing kernel methods microplates point collocation methods static flexural 3D microstructure-dependent analysis
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Interpreting uninterpretable predictors:kernel methods,Shtarkov solutions,and random forests 认领 引用 被引量：1
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作者 T.M.Le Bertrand Clarke 《Statistical Theory and Related Fields》 CSCD 2022年第1期10-28,共19页
Many of the best predictors for complex problems are typically regarded as hard to interpret physically.These include kernel methods,Shtarkov solutions,and random forests.We show that,despite the inability to interpre... Many of the best predictors for complex problems are typically regarded as hard to interpret physically.These include kernel methods,Shtarkov solutions,and random forests.We show that,despite the inability to interpret these three predictors to infinite precision,they can be asymptotically approximated and admit conceptual interpretations in terms of their mathe-matical/statistical properties.The resulting expressions can be in terms of polynomials,basis elements,or other functions that an analyst may regard as interpretable. 展开更多
关键词 Bayes boosting kernel methods random forest Shtarkov predictor stacking
Iterated rational quadratic kernel-High-order unscented Kalman filtering algorithm for spacecraft tracking 认领 引用
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作者 Xinru Liang Changsheng Gao +1 位作者 Wuxing Jing Ruoming An 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2025年第3期238-250,共13页
The high-speed development of space defense technology demands a high state estimation capacity for spacecraft tracking methods.However,reentry flight is accompanied by complex flight environments,which brings to the ... The high-speed development of space defense technology demands a high state estimation capacity for spacecraft tracking methods.However,reentry flight is accompanied by complex flight environments,which brings to the uncertain,complex,and strongly coupled non-Gaussian detection noise.As a result,there are several intractable considerations on the problem of state estimation tasks corrupted by complex non-Gaussian outliers for non-linear dynamics systems in practical application.To address these issues,a new iterated rational quadratic(RQ)kernel high-order unscented Kalman filtering(IRQHUKF)algorithm via capturing the statistics to break through the limitations of the Gaussian assumption is proposed.Firstly,the characteristic analysis of the RQ kernel is investigated in detail,which is the first attempt to carry out an exploration of the heavy-tailed characteristic and the ability on capturing highorder moments of the RQ kernel.Subsequently,the RQ kernel method is first introduced into the UKF algorithm as an error optimization criterion,termed the iterated RQ kernel-UKF(RQ-UKF)algorithm by derived analytically,which not only retains the high-order moments propagation process but also enhances the approximation capacity in the non-Gaussian noise problem for its ability in capturing highorder moments and heavy-tailed characteristics.Meanwhile,to tackle the limitations of the Gaussian distribution assumption in the linearization process of the non-linear systems,the high-order Sigma Points(SP)as a subsidiary role in propagating the state high-order statistics is devised by the moments matching method to improve the RQ-UKF.Finally,to further improve the flexibility of the IRQ-HUKF algorithm in practical application,an adaptive kernel parameter is derived analytically grounded in the Kullback-Leibler divergence(KLD)method and parametric sensitivity analysis of the RQ kernel.The simulation results demonstrate that the novel IRQ-HUKF algorithm is more robust and outperforms the existing advanced UKF with respect to the kernel method in reentry vehicle tracking scenarios under various noise environments. 展开更多
关键词 Kernel method Rational quadratic(RQ)kernel High-order sigma points Spacecraft Reentry vehicles
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ON NETWORK-BASED KERNEL METHODS FOR PROTEIN-PROTEIN INTERACTIONS WITH APPLICATIONS IN PROTEIN FUNCTIONS PREDICTION 认领 引用 被引量：1
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作者 Limin LI Waiki CHING +1 位作者 Yatming CHAN Hiroshi MAMITSUKA 《Journal of Systems Science & Complexity》 SCIE EI 2010年第5期917-930,共14页
Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kerne... Predicting protein functions is an important issue in the post-genomic era. This paper studies several network-based kernels including local linear embedding （LLE） kernel method, diffusion kernel and laplacian kernel to uncover the relationship between proteins functions and protein-protein interactions （PPI）. The author first construct kernels based on PPI networks, then apply support vector machine （SVM） techniques to classify proteins into different functional groups. The 5-fold cross validation is then applied to the selected 359 GO terms to compare the performance of different kernels and guilt-by-association methods including neighbor counting methods and Chi-square methods. Finally, the authors conduct predictions of functions of some unknown genes and verify the preciseness of our prediction in part by the information of other data source. 展开更多
关键词 Diffusion kernel kernel method Laplacian kernel local linear embedding （LLE） kernel protein function prediction support vector machine.
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h-ADAPTIVITY ANALYSIS BASED ON MULTIPLE SCALE REPRODUCING KERNEL PARTICLE METHOD 认领 引用 被引量：4
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作者 ZHANG Zhi-qian ZHOU Jin-xiong +2 位作者 WANG Xue-ming ZHANG Yan-fen ZHANG Ling 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2005年第8期1064-1071,共8页
An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes（SNN） and local-Delaunay-tri... An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes（SNN） and local-Delaunay-triangulation（LDT） techniques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h- adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h- adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property. 展开更多
关键词 meshfree method reproducing kernel particle method multiresolution analysis adaptive analysis
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An interpolating reproducing kernel particle method for two-dimensional scatter points 认领 引用 被引量：3
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作者 秦义校 刘营营 +1 位作者 李中华 杨明 《Chinese Physics B》 SCIE EI CAS CSCD 2014年第7期238-241,共4页
An interpolating reproducing kernel particle method for two-dimensional （2D） scatter points is introduced. It elim- inates the dependency of gridding in numerical calculations. The interpolating shape function in th... An interpolating reproducing kernel particle method for two-dimensional （2D） scatter points is introduced. It elim- inates the dependency of gridding in numerical calculations. The interpolating shape function in the interpolating repro- ducing kernel particle method satisfies the property of the Kronecker delta function. This method offers a mathematics basis for recognition technology and simulation analysis, which can be expressed as simultaneous differential equations in science or project problems. Mathematical examples are given to show the validity of the interpolating reproducing kernel particle method. 展开更多
关键词 interpolating reproducing kernel particle method point interpolating characteristic scatter points
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h-ADAPTIVITY ANALYSIS BASED ON MULTIPLE SCALE REPRODUCING KERNEL PARTICLE METHOD 认领 引用 被引量：2
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作者 张智谦 周进雄 +2 位作者 王学明 张艳芬 张陵 《应用数学和力学》 北大核心 2005年第8期972-978,共7页
An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes(SNN) and local-Delaunay-trian... An h-adaptivity analysis scheme based on multiple scale reproducing kernel particle method was proposed, and two node refinement strategies were constructed using searching-neighbor-nodes(SNN) and local-Delaunay-triangulation(LDT) tech-niques, which were suitable and effective for h-adaptivity analysis on 2-D problems with the regular or irregular distribution of the nodes. The results of multiresolution and h-adaptivity analyses on 2-D linear elastostatics and bending plate problems demonstrate that the improper high-gradient indicator will reduce the convergence property of the h-adaptivity analysis, and that the efficiency of the LDT node refinement strategy is better than SNN, and that the presented h-adaptivity analysis scheme is provided with the validity, stability and good convergence property. 展开更多
关键词 无网格方法 再生核质点法 多分辨分析 自适应分析
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Application of Reproducing Kernel Particle Method in an Analysis of Elasto-plastic Deformation Under Taylor Impact 认领 引用 被引量：1
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作者 ZHAO Guang-ming SONG Shun-cheng MENG Xiang-rui 《Journal of China University of Mining and Technology》 2006年第4期485-489,共5页
The Reproducing Kernel Particle Method (RKPM) is one of several new meshless numerical methods de- veloped internationally in recent years. The ideal elasto-plastic constitutive model of material under a Taylor impact... The Reproducing Kernel Particle Method (RKPM) is one of several new meshless numerical methods de- veloped internationally in recent years. The ideal elasto-plastic constitutive model of material under a Taylor impact is characterized by the Jaumann stress- and strain-rates. An updated Lagrangian format is used for the calculation in a nu- merical analysis. With the RKPM, this paper deals with the calculation model for the Taylor impact and deduces the control equation for the impact process. A program was developed to simulate numerically the Taylor impact of projec- tiles composed of several kinds of material. The simulation result is in good accordance with both the test results and the Taylor analysis outcome. Since the meshless method is not limited by meshes, it is believed to be widely applicable to such complicated processes as the Taylor impact, including large deformation and strain and to the study of the dy- namic qualities of materials. 展开更多
关键词 Reproducing Kernel Particle Method Taylor impact large deformation meshless method
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ANALYSIS OF THREE-DIMENSIONAL UPSETTING PROCESS BY THE RIGID-PLASTIC REPRODUCING KERNEL PARTICLE METHOD 认领 引用 被引量：2
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作者 Y. H. Liu J. Chen S. Yu X. W. Chen 《Acta Metallurgica Sinica(English Letters)》 EI CAS 2006年第5期371-378,共8页
A meshless approach, called the rigid-plastic reproducing kernel particle method （RKPM）, is presented for three-dimensional （3D） bulk metal forming simulation. The approach is a combination of RKPM with the flow t... A meshless approach, called the rigid-plastic reproducing kernel particle method （RKPM）, is presented for three-dimensional （3D） bulk metal forming simulation. The approach is a combination of RKPM with the flow theory of 3D rigid-plastic mechanics. For the treatments of essential boundary conditions and incompressibility constraint, the boundary singular kernel method and the modified penalty method are utilized, respectively. The arc-tangential friction model is employed to treat the contact conditions. The compression of rectangular blocks, a typical 3D upsetting operation, is analyzed for different friction conditions and the numerical results are compared with those obtained using commercial rigid-plastic FEM （finite element method） software Deform^3D. As results show, when handling 3D plastic deformations, the proposed approach eliminates the need of expensive meshing and remeshing procedures which are unavoidable in conventional FEM and can provide results that are in good agreement with finite element predictions. 展开更多
关键词 meshless reproducing kernel particle method（RKPM） three-dimensional upsetting incompressibility modified penalty method
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Analysis of variable coefficient advection-diffusion problems via complex variable reproducing kernel particle method 认领 引用 被引量：1
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作者 翁云杰 程玉民 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第9期197-202,共6页
The complex variable reproducing kernel particle method （CVRKPM） of solving two-dimensional variable coefficient advection-diffusion problems is presented in this paper. The advantage of the CVRKPM is that the shape... The complex variable reproducing kernel particle method （CVRKPM） of solving two-dimensional variable coefficient advection-diffusion problems is presented in this paper. The advantage of the CVRKPM is that the shape function of a two-dimensional problem is formed with a one-dimensional basis function. The Galerkin weak form is employed to obtain the discretized system equation, and the penalty method is used to apply the essential boundary conditions. Then the corresponding formulae of the CVRKPM for two-dimensional variable coefficient advection-diffusion problems are obtained. Two numerical examples are given to show that the method in this paper has greater accuracy and computational efficiency than the conventional meshless method such as reproducing the kernel particle method （RKPM） and the element- free Galerkin （EFG） method. 展开更多
关键词 meshless method reproducing kernel particle method （RKPM） complex variable reproducingkernel particle method （CVRKPM） advection-diffusion problem
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Splitting Rolling Simulated by Reproducing Kernel Particle Method 认领 引用
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作者 CUI Qing-ling LIU Xiang-hua WANG Guo-dong 《Journal of Iron and Steel Research International》 SCIE CAS 2007年第3期42-46,共5页
During splitting rolling simulation,re-meshing is necessary to prevent the effect of severe mesh distortion when the conventional finite element method is used.However,extreme deformation cannot be solved by the finit... During splitting rolling simulation,re-meshing is necessary to prevent the effect of severe mesh distortion when the conventional finite element method is used.However,extreme deformation cannot be solved by the finite element method in splitting rolling.The reproducing kernel particle method can solve this problem because the continuum body is discretized by a set of nodes,and a finite element mesh is unnecessary,and there is no explicit limitation of mesh when the metal is split.To ensure stability in the large deformation elastoplastic analysis,the Lagrange material shape function was introduced.The transformation method was utilized to impose the essential boundary conditions.The splitting rolling method was simulated and the simulation results were in accordance with the experimental ones in the literature. 展开更多
关键词 elasto-plasticity large deformation reproducing kernel particle method splitting rolling
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Stability and dispersion analysis of reproducing kernel collocation method for transient dynamics 认领 引用
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作者 罗汉中 刘学文 黄醒春 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2011年第6期777-788,共12页
A reproducing kernel collocation method based on strong formulation is introduced for transient dynamics. To study the stability property of this method, an algorithm based on the von Neumann hypothesis is proposed to... A reproducing kernel collocation method based on strong formulation is introduced for transient dynamics. To study the stability property of this method, an algorithm based on the von Neumann hypothesis is proposed to predict the critical time step. A numerical test is conducted to validate the algorithm. The numerical critical time step and the predicted critical time step are in good agreement. The results are compared with those obtained based on the radial basis collocation method, and they axe in good agreement. Several important conclusions for choosing a proper support size of the reproducing kernel shape function are given to improve the stability condition. 展开更多
关键词 reproducing kernel collocation method （RKCM） stability analysis disper-sion analysis transient dynamics
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