• 제목/요약/키워드: rate of convergence

검색결과 3,458건 처리시간 0.026초

An Improved Element Removal Method for Evolutionary Structural Optimization

  • Han, Seog-Young
    • Journal of Mechanical Science and Technology
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    • 제14권9호
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    • pp.913-919
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    • 2000
  • The purpose of this study was to develop a new element removal method for ESO (Evolutionary Structural Optimization), which is one of the topology optimization methods. ESO starts with the maximum allowable design space and the optimal topology emerges by a process of removal of lowly stressed elements. The element removal ratio of ESO is fixed throughout topology optimization at 1 or 2%. BESO (bidirectional ESO) starts with either the least number of elements connecting the loads to the supports, or an initial design domain that fits within the maximum allowable domain, and the optimal topology evolves by adding or subtracting elements. But the convergence rate of BESO is also very slow. In this paper, a new element removal method for ESO was developed for improvement of the convergence rate. Then it was applied to the same problems as those in papers published previously. From the results, it was verified that the convergence rate was significantly improved compared with ESO as well as BESO.

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통계처리를 활용한 터널 내공변위의 분석에 관한 연구 (Estimation of Tunnel Convergence Using Statistical Analysis)

  • 김종우
    • 터널과지하공간
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    • 제13권2호
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    • pp.108-116
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    • 2003
  • 백악기 경상계 안산암과 불국사 화강암류가 주로 분포하는 지반에서 시공된 터널의 내공변위 계측자료를 분석하였다. 터널 주변 암반을 RMR법에 의한 다섯 가지 암반등급으로 구분하고 각 등급에 포함된 계측자료들을 통계처리하여 암반등급별 내공변위의 회귀분석을 실시하였다. 연구 결과. 로그함수보다는 지수함수의 상관계수 가 더 크며, 연약한 암반등급일수록 내공변위의 크기와 표준편차가 크게 나타났다. 또한, 최종내공변위에 대한 최대변위속도 및 초기내공변위의 관계를 도출하였으며, 이 중에서 최종내공변위와 최대변위속도의 상관계수는 0.87로 나타나 이들은 비교적 높은 상관성을 가지는 것으로 확인되었다

적응잡음제거기의 정상상태 성능 및 수렴율 향상에 관한 연구 (A study on improvement of steady-state peformance and convergence rate in an adaptive noise canceller)

  • 배종갑;김창기;박장식;손경식
    • 전자공학회논문지S
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    • 제34S권4호
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    • pp.42-49
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    • 1997
  • A conventional adaptive noise canceller (ANC) using LMS algorithm suffers from the misadjustment of adaptive filter weights due to the gradient-estimate noise by input speech signal at steady state. In this paper, an ANC is proposed which uses the combination of VSLMS (variable step size LMS) and SA (sign algorithm) to improve steady state performance and convergence rate. SA algorithm is applied in speech region to prevent the weights from perturbing by output speech of ANC and VSLMS algorithm is applied to improve convergence rate and channel tracking ability in silence region and adaptive transient region. In compute rsimulation, the performance of the proposed VSLMS-SA combination algorithm is much better than LMS algorithm and the algorithm, recently proposed by greenberg, with adaptation step-size parameter determine dby sum method in convergence rate, channel tracking and steady state performance.

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NEWTON'S METHOD FOR SOLVING A QUADRATIC MATRIX EQUATION WITH SPECIAL COEFFICIENT MATRICES

  • Seo, Sang-Hyup;Seo, Jong-Hyun;Kim, Hyun-Min
    • 호남수학학술지
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    • 제35권3호
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    • pp.417-433
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    • 2013
  • We consider the iterative solution of a quadratic matrix equation with special coefficient matrices which arises in the quasibirth and death problem. In this paper, we show that the elementwise minimal positive solvent of the quadratic matrix equations can be obtained using Newton's method if there exists a positive solvent and the convergence rate of the Newton iteration is quadratic if the Fr$\acute{e}$chet derivative at the elementwise minimal positive solvent is nonsingular. Although the Fr$\acute{e}$chet derivative is singular, the convergence rate is at least linear. Numerical experiments of the convergence rate are given.

TWO-LAYER MUTI-PARAMETERIZED SCHWARZ ALTERNATING METHOD

  • Kim, Sang-Bae
    • Journal of applied mathematics & informatics
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    • 제9권1호
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    • pp.101-124
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    • 2002
  • The convergence rate of a numerical procedure barred on Schwarz Alternating Method (SAM) for solving elliptic boundary value problems (BVP's) depends on the selection of the interface conditions applied on the interior boundaries of the overlapping subdomains. It hee been observed that the Robin condition(mixed interface condition), controlled by a parameter, can optimize SAM's convergence rate. Since the convergence rate is very sensitive to the parameter, Tang[17] suggested another interface condition called over-determined interface condition. Based on the over-determined interface condition, we formulate the two-layer multi-parameterized SAM. For the SAM and the one-dimensional elliptic model BVP's, we determine analytically the optimal values of the parameters. For the two-dimensional elliptic BVP's , we also formulate the two-layer multi-parameterized SAM and suggest a choice of multi-parameter to produce good convergence rate .

TWO-LAYER MUTI-PARAMETERIZED SCHWARZ ALTERNATING METHOD FOR TWO-DIMENSIONAL PROBLEMS

  • Kim, Sang-Bae
    • Journal of applied mathematics & informatics
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    • 제30권3_4호
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    • pp.477-488
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    • 2012
  • The convergence rate of a numerical procedure based on Schwarz Alternating Method(SAM) for solving elliptic boundary value problems depends on the selection of the interface conditions applied on the interior boundaries of the overlapping subdomains. It has been observed that the mixed interface condition, controlled by a parameter, can optimize SAM's convergence rate. In [8], one introduced the two-layer multi-parameterized SAM and determined the optimal values of the multi-parameters to produce the best convergence rate for one-dimensional elliptic boundary value problems. In this paper, we present a method which utilizes the one-dimensional result to get the optimal convergence rate for the two-dimensional problem.

TWO-DIMENSIONAL MUTI-PARAMETERIZED SCHWARZ ALTERNATING METHOD

  • Kim, Sang-Bae
    • Journal of applied mathematics & informatics
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    • 제29권1_2호
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    • pp.161-171
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    • 2011
  • The convergence rate of a numerical procedure based on Schwarz Alternating Method(SAM) for solving elliptic boundary value problems depends on the selection of the interface conditions applied on the interior boundaries of the overlapping subdomains. It has been observed that the Robin condition (mixed interface condition), controlled by a parameter, can optimize SAM's convergence rate. In [7], one had formulated the multi-parameterized SAM and determined the optimal values of the multi-parameters to produce the best convergence rate for one-dimensional elliptic boundary value problems. However it was not successful for two-dimensional problem. In this paper, we present a new method which utilizes the one-dimensional result to get the optimal convergence rate for the two-dimensional problem.

SECOND ORDER REGULAR VARIATION AND ITS APPLICATIONS TO RATES OF CONVERGENCE IN EXTREME-VALUE DISTRIBUTION

  • Lin, Fuming;Peng, Zuoxiang;Nadarajah, Saralees
    • 대한수학회보
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    • 제45권1호
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    • pp.75-93
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    • 2008
  • The rate of convergence of the distribution of order statistics to the corresponding extreme-value distribution may be characterized by the uniform and total variation metrics. de Haan and Resnick [4] derived the convergence rate when the second order generalized regularly varying function has second order derivatives. In this paper, based on the properties of the generalized regular variation and the second order generalized variation and characterized by uniform and total variation metrics, the convergence rates of the distribution of the largest order statistic are obtained under weaker conditions.

Enhanced Fuzzy Single Layer Perceptron

  • Chae, Gyoo-Yong;Eom, Sang-Hee;Kim, Kwang-Baek
    • Journal of information and communication convergence engineering
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    • 제2권1호
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    • pp.36-39
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    • 2004
  • In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.