• 제목/요약/키워드: linear convergence

검색결과 1,312건 처리시간 0.029초

NUMERICAL SOLUTIONS FOR MODELS OF LINEAR ELASTICITY USING FIRST-ORDER SYSTEM LEAST SQUARES

  • Lee, Chang-Ock
    • Korean Journal of Mathematics
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    • 제7권2호
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    • pp.245-269
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    • 1999
  • Multigrid method and acceleration by conjugate gradient method for first-order system least squares (FOSLS) using bilinear finite elements are developed for various boundary value problems of planar linear elasticity. They are two-stage algorithms that first solve for the displacement flux variable, then for the displacement itself. This paper focuses on solving for the displacement flux variable only. Numerical results show that the convergence is uniform even as the material becomes nearly incompressible. Computations for convergence factors and discretization errors are included. Heuristic arguments to improve the convergences are discussed as well.

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BERRY-ESSEEN BOUND FOR MLE FOR LINEAR STOCHASTIC DIFFERENTIAL EQUATIONS DRIVEN BY FRACTIONAL BROWNIAN MOTION

  • RAO B.L.S. PRAKASA
    • Journal of the Korean Statistical Society
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    • 제34권4호
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    • pp.281-295
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    • 2005
  • We investigate the rate of convergence of the distribution of the maximum likelihood estimator (MLE) of an unknown parameter in the drift coefficient of a stochastic process described by a linear stochastic differential equation driven by a fractional Brownian motion (fBm). As a special case, we obtain the rate of convergence for the case of the fractional Ornstein- Uhlenbeck type process studied recently by Kleptsyna and Le Breton (2002).

Prediction of calcium and phosphorus requirements for pigs in different bodyweight ranges using a meta-analysis

  • Jeon, Se Min;Hosseindoust, Abdolreza;Ha, Sang Hun;Kim, Tae Gyun;Mun, Jun Young;Moturi, Joseph;Lee, SuHyup;Choi, Yo Han;Lee, Sang Deok;Sa, Soo Jin;Kim, Jin Soo
    • Journal of Animal Science and Technology
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    • 제63권4호
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    • pp.827-840
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    • 2021
  • Several studies have focused on Ca and P requirements for pigs. These requirements are estimated from their retention and bone formation. However, modern pig breeds have different responses to dietary Ca and P than traditional breeds, and their requirements are expected to change on an annual basis. Besides individual Ca and P needs, the Ca to P ratio (Ca/P) is an important factor in determining requirements. This study aimed to implement a linear and quadratic regression analysis to estimate Ca and P requirements based on average daily gain (ADG), apparent total tract digestibility (ATTD) of Ca (ATTD-Ca), ATTD of P (ATTD-P), and crude protein (CP) digestibility. Results show that Ca/P had linear and quadratic effects on ADG in the phytase-supplemented (PS) group in both the 6-11 kg and 11-25 kg categories. In the latter category, the CP digestibility was linearly increased in response to increasing Ca/P in the without-phytase (WP) group. In the 25-50 kg category, there was a linear response of ADG and linear and quadratic responses of CP digestibility to Ca/P in the PS group, while a linear and quadratic increase in CP digestibility and a quadratic effect on ATTD-Ca were observed in the WP group. In the 50-75 kg category, Ca/P had significant quadratic effects on ADG in the PS and WP groups, along with significant linear and quadratic effects on ATTD-Ca. In addition, Ca/P had significant quadratic effects on ATTD-P and led to a significant linear and quadratic increase in the CP digestibility in the WP group. In the 75-100 kg category, analysis showed a significant decrease in ATTD-Ca and ATTD-P in the PS and WP groups; in the latter, ATTD-P and ATTD-Ca were linearly decreased by increasing Ca/P. In conclusion, our equations predicted a higher Ca/P in the 6-25 kg bodyweight categories and a lower Ca/P in the 50-100 kg category than that recommended in the literature.

Imputation Method Using Local Linear Regression Based on Bidirectional k-nearest-components

  • Yonggeol, Lee
    • Journal of information and communication convergence engineering
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    • 제21권1호
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    • pp.62-67
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    • 2023
  • This paper proposes an imputation method using a bidirectional k-nearest components search based local linear regression method. The bidirectional k-nearest-components search method selects components in the dynamic range from the missing points. Unlike the existing methods, which use a fixed-size window, the proposed method can flexibly select adjacent components in an imputation problem. The weight values assigned to the components around the missing points are calculated using local linear regression. The local linear regression method is free from the rank problem in a matrix of dependent variables. In addition, it can calculate the weight values that reflect the data flow in a specific environment, such as a blackout. The original missing values were estimated from a linear combination of the components and their weights. Finally, the estimated value imputes the missing values. In the experimental results, the proposed method outperformed the existing methods when the error between the original data and imputation data was measured using MAE and RMSE.

Convergence Characteristics of the Crank-Nicolson-Galerkin Scheme for Linear Parabolic Systems

  • Cho, Jin-Rae;Ha, Dae-Yul;Kim, Tae-Jong
    • Journal of Mechanical Science and Technology
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    • 제16권10호
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    • pp.1264-1275
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    • 2002
  • This paper is concerned with the investigation on the stability and convergence characteristics of the Crank-Nicolson-Galerkin scheme that is widely being employed for the numerical approximation of parabolic-type partial differential equations. Here, we present the theoretical analysis on its consistency and convergence, and we carry out the numerical experiments to examine the effect of the time-step size △t on the h- and P-convergence rates for various mesh sizes h and approximation orders P. We observed that the optimal convergence rates are achieved only when △t, h and P are chosen such that the total error is not affected by the oscillation behavior. In such case, △t is in linear relation with DOF, and furthermore its size depends on the singularity intensity of problems.

퍼지 결합 다항식 뉴럴 네트워크 기반 패턴 분류기 설계 (The Design of Pattern Classification based on Fuzzy Combined Polynomial Neural Network)

  • 노석범;장경원;안태천
    • 전기학회논문지
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    • 제63권4호
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    • pp.534-540
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    • 2014
  • In this paper, we propose a fuzzy combined Polynomial Neural Network(PNN) for pattern classification. The fuzzy combined PNN comes from the generic TSK fuzzy model with several linear polynomial as the consequent part and is the expanded version of the fuzzy model. The proposed pattern classifier has the polynomial neural networks as the consequent part, instead of the general linear polynomial. PNNs are implemented by stacking the simple polynomials dynamically. To implement one layer of PNNs, the various types of simple polynomials are used so that PNNs have flexibility and versatility. Although the structural complexity of the implemented PNNs is high, the PNNs become a high order-multi input polynomial finally. To estimate the coefficients of a polynomial neuron, The weighted linear discriminant analysis. The output of fuzzy rule system with PNNs as the consequent part is the linear combination of the output of several PNNs. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets.

A Study on Linear Matrix Inequalities Robust Active Suspension Control System Design Algorithm

  • Park, Jung-Hyen
    • Journal of information and communication convergence engineering
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    • 제6권1호
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    • pp.105-109
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    • 2008
  • A robust optimal control system design algorithm in active suspension equipment adopting linear matrix inequalities control system design theory is presented. The validity of the linear matrix inequalities robust control system design in active suspension system through the numerical examples is also investigated.

선형 등가정하중을 이용한 비선형 거동 구조물의 최적설계 (I) - 알고리듬 - (Structural Optimization for Non-Linear Behavior Using Equivalent Static Loads (I))

  • 박기종;박경진
    • 대한기계학회논문집A
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    • 제29권8호
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    • pp.1051-1060
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    • 2005
  • Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.