• 제목/요약/키워드: Steepest Descent

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Steepest Descent Method를 이용한 정전계 문제의 해석 (On the Analysis of Electrostatic Problems Using a Steepest Descent Method)

  • 안지용;정구철;김정기
    • 한국통신학회논문지
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    • 제11권6호
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    • pp.396-401
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    • 1986
  • Steepest Descent Method를 정전계 문제의 해석에 적용하였다. 반복법과 moment method와 같은 direct method와의 차이점을 기술하였다. 반복법을 이용하여 정확한 해에 단조적으로 수렴하며 대형 system의 해석에 적합함을 보였다. 정전계 문제에 대해서 해석한 수치결과를 제시하였으며 moment solution과 잘 일치하고 있음을 보였다.

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새로운 성능지수 함수에 대한 직강하 적응필터 (Novel steepest descent adaptive filters derived from new performance function)

  • 전병을;박동조
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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    • pp.823-828
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    • 1992
  • A novel steepest descent adaptive filter algorithm, which uses the instantaneous stochastic gradient for the steepest descent direction, is derived from a newly devised performance index function. The performance function for the new algorithm is improved from that for the LMS in consideration that the stochastic steepest descent method is utilized to minimize the performance index iterativly. Through mathematical analysis and computer simulations, it is verified that there are substantial improvements in convergence and misadjustments even though the computational simplicity and the robustness of the LMS algorithm are hardly sacrificed. On the other hand, the new algorithm can be interpreted as a variable step size adaptive filter, and in this respect a heuristic method is proposed in order to reduce the noise caused by the step size fluctuation.

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MOTION VECTOR DETECTION ALGORITHM USING THE STEEPEST DESCENT METHOD EFFECTIVE FOR AVOIDING LOCAL SOLUTIONS

  • Konno, Yoshinori;Kasezawa, Tadashi
    • 한국방송∙미디어공학회:학술대회논문집
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    • 한국방송공학회 2009년도 IWAIT
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    • pp.460-465
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    • 2009
  • This paper presents a new algorithm that includes a mechanism to avoid local solutions in a motion vector detection method that uses the steepest descent method. Two different implementations of the algorithm are demonstrated using two major search methods for tree structures, depth first search and breadth first search. Furthermore, it is shown that by avoiding local solutions, both of these implementations are able to obtain smaller prediction errors compared to conventional motion vector detection methods using the steepest descent method, and are able to perform motion vector detection within an arbitrary upper limit on the number of computations. The effects that differences in the search order have on the effectiveness of avoiding local solutions are also presented.

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두가지 gradient 방법의 벡터 선형 예측기에 대한 적용 비교 (Comparison with two Gradient Methods through the application to the Vector Linear Predictor)

  • 신광균;양승인
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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    • pp.1595-1597
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    • 1987
  • Two gradient methods, steepest descent method and conjugate gradient descent method, are compar ed through application to vector linear predictors. It is found that the convergence rate of the conju-gate gradient descent method is much faster than that of the steepest descent method.

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최속 강하법을 이용한 흡수식 냉동공조시스템 제어 (Control of the Absorption Air Conditioning System by Using Steepest Descent Method)

  • 한도영;김진
    • 설비공학논문집
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    • 제15권6호
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    • pp.495-501
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    • 2003
  • Control algorithms for the absorption air conditioning system may be developed by using dynamic models of the system. The simplified effective dynamic models, which can predict the dynamic behaviors of the system, may help to develop effective control algorithms for the system. In this study, control algorithms for an absorption air conditioning system were developed by using a dynamic simulation program. A cooling water inlet temperature control algorithm, a chilled water outlet temperature control algorithm, and a supply air temperature control algorithm, were developed and analyzed. The steepest descent method was used as an optimal algorithm. The simulation results showed energy savings and the effective controls of an absorption air conditioning system.

STRONG CONVERGENCE OF THE MODIFIED HYBRID STEEPEST-DESCENT METHODS FOR GENERAL VARIATIONAL INEQUALITIES

  • Yao, Yonghong;Noor, Muhammad Aslam
    • Journal of applied mathematics & informatics
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    • 제24권1_2호
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    • pp.179-190
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    • 2007
  • In this paper, we consider the general variational inequality GVI(F, g, C), where F and g are mappings from a Hilbert space into itself and C is the fixed point set of a nonexpansive mapping. We suggest and analyze a new modified hybrid steepest-descent method of type method $u_{n+l}=(1-{\alpha}+{\theta}_{n+1})Tu_n+{\alpha}u_n-{\theta}_{n+1g}(Tu_n)-{\lambda}_{n+1}{\mu}F(Tu_n),\;n{\geq}0$. for solving the general variational inequalities. The sequence $\{x_n}\$ is shown to converge in norm to the solutions of the general variational inequality GVI(F, g, C) under some mild conditions. Application to constrained generalized pseudo-inverse is included. Results proved in the paper can be viewed as an refinement and improvement of previously known results.

MOS 센서어레이를 이용한 냄새 분류 및 농도추정을 위한 LM-BP 알고리즘 응용 (LM-BP algorithm application for odour classification and concentration prediction using MOS sensor array)

  • 최찬석;변형기;김정도
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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    • pp.210-210
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    • 2000
  • In this paper, we have investigated the properties of multi-layer perceptron (MLP) for odour patterns classification and concentration estimation simultaneously. When the MLP may be has a fast convergence speed with small error and excellent mapping ability for classification, it can be possible to use for classification and concentration prediction of volatile chemicals simultaneously. However, the conventional MLP, which is back-Propagation of error based on the steepest descent method, was difficult to use for odour classification and concentration estimation simultaneously, because it is slow to converge and may fall into the local minimum. We adapted the Levenberg-Marquardt(LM) algorithm [4,5] having advantages both the steepest descent method and Gauss-Newton method instead of the conventional steepest descent method for the simultaneous classification and concentration estimation of odours. And, We designed the artificial odour sensing system(Electronic Nose) and applied LM-BP algorithm for classification and concentration prediction of VOC gases.

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The Optimal Control of an Absorption Air Conditioning System by Using the Steepest Descent Method

  • Han Doyoung;Kim Jin
    • International Journal of Air-Conditioning and Refrigeration
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    • 제12권3호
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    • pp.123-130
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    • 2004
  • Control algorithms for an absorption air conditioning system may be developed by using dynamic models of the system. The simplified effective dynamic models, which can predict the dynamic behaviors of the system, may help to develop effective control algorithms for the system. In this study, control algorithms for an absorption air conditioning system were developed by using a dynamic simulation program. A cooling water inlet temperature control algorithm, a chilled water outlet temperature control algorithm, and a supply air temperature control algorithm, were developed and analyzed. The steepest descent method was used as an optimal algorithm. Simulation results showed energy savings and the effective controls of an absorption air conditioning system.

OPTIMIZATION FOR THE BUBBLE STABILIZED LEGENDRE GALERKIN METHODS BY STEEPEST DESCENT METHOD

  • Kim, Seung Soo;Lee, Yong Hun;Oh, Eun Jung
    • 호남수학학술지
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    • 제36권4호
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    • pp.755-766
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    • 2014
  • In the discrete formulation of the bubble stabilized Legendre Galerkin methods, the system of equations includes the artificial viscosity term as the parameter. We investigate the estimation of this parameter to get the optimal solution which minimizes the maximum error. Some numerical results are reported.