• Journal of applied mathematics & informatics
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• v.4 no.1
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• pp.83-108
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• 1997

Chebysheff-Halley methods are probably the best known cubically convergent iterative procedures for solving nonlinear equa-tions. These methods however require an evaluation of the second Frechet-derivative at each step which means a number of function eval-uations proportional to the cube of the dimension of the space. To re-duce the computational cost we replace the second Frechet derivative with a fixed bounded bilinear operator. Using the majorant method and Newton-Kantorovich type hypotheses we provide sufficient condi-tions for the convergence of our method to a locally unique solution of a nonlinear equation in Banach space. Our method is shown to be faster than Newton's method under the same computational cost. Finally we apply our results to solve nonlinear integral equations appearing in radiative transfer in connection with the problem of determination of the angular distribution of the radiant-flux emerging from a plane radiation field.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.666-671
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• 2010

Multi-layer perceptron neural networks have been constructed to classify four types of defects in steam generator tubes. Three features are extracted from the signals of the eddy current testing method. These include maximum impedance, phase angle at the point of maximum impedance, and an angle between the point of maximum impedance and the point of half the maximum impedance. Two hundred sets of these features are used for training and assessing the networks. Two approaches are involved to train the networks and to classify the defect type. One is the conjugate gradient method and the other is the Broydon-Fletcher-Goldfarb-Shanno method which is recognized as the most popular algorithm of quasi-Newton methods. It is found from the computation results that the training time of the Broydon-Fletcher-Goldfarb-Shanno method is much faster than that of the conjugate gradient method in most cases. On the other hand, no significant difference of the classification performance between the two methods is observed.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1624-1627
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• 1997

The 6-DOF parallel manipulator is a closed-kindmatic chain robot manipulator that is capable of providing high structural rigidity and positional accuracy. Because of its advantage, the parallel manipulator have been widely used in many engineering applications such as vehicle/flight driving simulators, rogot maniplators, attachment tool of machining centers, etc. However, the kinematic analysis for the implementation of a real-time controller has some problem because of the lack of an efficient lagorithm for solving its highly nonliner forward kinematic equation, which provides the translational and orientational attitudes of the moveable upper platform from the lenght of manipulator linkages. Generally, Newton-Raphson method has been widely sued to solve the forward kinematic problem but the effectiveness of this methodology depend on how to set initial values. This paper proposes a hybrid method using genetic algorithm(GA) and Newton-Raphson method to solve forward kinematics. That is, the initial values of forward kinematics solution are determined by adopting genetic algorithm which can search grobally optimal solutions. Since determining this values, the determined values are used in Newton-Raphson method for real time calcuation.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.772-777
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• 2017

In this paper, the goal of optimization design is efficiency increase and vibration optimization. For this purpose, shape optimization design was performed using Quasi-Newton method, which is one of optimization techniques, and it was verified through finite element analysis In addition, modal analysis of the stator was performed to estimate the natural frequency. through the vibration experiment, the vibration was verified for cogging torque and RMF analysis. Finally, optimal and basic models were compared and analyzed for efficeincy characteristics.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1181-1188
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• 2017

This paper proposes the adoption of Monte Carlo perturbation theory to approximate the Jacobian matrix of coupled neutronics/thermal-hydraulics problems. The projected Jacobian is obtained from the eigenvalue decomposition of the fission matrix, and it is adopted to solve the coupled problem via the Newton method. This avoids numerical differentiations commonly adopted in Jacobian-free Newton-Krylov methods that tend to become expensive and inaccurate in the presence of Monte Carlo statistical errors in the residual. The proposed approach is presented and preliminarily demonstrated for a simple two-dimensional pressurized water reactor case study.

• Proceedings of the KSME Conference
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• 2004.11a
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• pp.1288-1293
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• 2004

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach of adopting the genetic algorithm as an initial value selector, whereas using the conjugate-gradient method and Newton method to reduce their dependence on the initial value.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1394-1395
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• 2011

Now a days has problem of energy resource shortage and environmental pollution. We are looking up various solution plans regarding this problem. Solar power is one of the solution to solve the energy shortage problem. But solar power is affected by the weather. So variation weather we can use Maximum Power Point Tracing device for solar PV system. In this paper, New MPPT method is proposed to tracking MPP at low and high insolation by combining constant voltage method with Newton Method method.

• 한국전산유체공학회:학술대회논문집
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• 1998.11a
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• pp.153-159
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• 1998

The Newton-Krylov method on the unstructured grid flow solver using the cell-centered spatial discretization oi compressible Euler equations is presented. This flow solver uses the reconstructed primitive variables to get the higher order solutions. To get the quadratic convergence of Newton method with this solver, the careful linearization of face flux is performed with the reconstructed flow variables. The GMRES method is used to solve large sparse matrix and to improve the performance ILU preconditioner is adopted and vectorized with level scheduling algorithm. To get the quadratic convergence with the higher order schemes and to reduce the memory storage. the matrix-free implementation and Barth's matrix-vector method are implemented and compared with the traditional matrix-vector method. The convergence and computing times are compared with each other.

• 한국전산유체공학회:학술대회논문집
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• 2003.10a
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• pp.33-34
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• 2003

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.8 s.239
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• pp.903-910
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• 2005

Inverse radiation problems are solved for estimating the boundary conditions such as temperature distribution and wall emissivity in axisymmetric absorbing, emitting and scattering medium, given the measured incident radiative heat fluxes. Various regularization methods, such as hybrid genetic algorithm, conjugate-gradient method and finite-difference Newton method, were adopted to solve the inverse problem, while discussing their features in terms of estimation accuracy and computational efficiency. Additionally, we propose a new combined approach that adopts the hybrid genetic algorithm as an initial value selector and uses the finite-difference Newton method as an optimization procedure.