• Structural Engineering and Mechanics
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• v.45 no.5
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• pp.613-629
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• 2013

In this article we will present a method to directly evaluate the critical point of a non-linear system by using the solution of a polynomial eigenvalue approximation as a starting point for an iterative non-linear system solver. This method will be used to evaluate out-of-plane buckling properties of truss structures for which the lateral displacement of the upper chord has been prevented. The aim is to assess for a number of example structures whether or not the linearized eigenvalue solution gives a relevant starting point for an iterative non-linear system solver in order to find the minimum positive critical load.

• Journal of applied mathematics & informatics
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• v.38 no.5_6
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• pp.489-506
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• 2020

The spatial fractional diffusion equation can be discretized by employing the implicit finite difference scheme using the shifted Grünwald formula. The discretized linear system is obtained, whose the coefficient matrix has a diagonal-plus-Toeplitz structure. In order to solve the diagonal-plus-Toeplitz linear system, on the basis of circulant and skew-circulant splitting (CSCS splitting), we construct a new and efficient iterative method, called DSCS iterative methods, which have two parameters. Than we prove the convergence of DSCS methods. As a focus, we derive the simple and effective values of two optimal parameters under some restrictions. Some numerical experiments are carried out to illustrate the validity and accuracy of the new methods.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.449-465
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• 1994

The two common numerical methods to approximate the solution of partial differential equations are the finite element method and the finite difference method. They both lead to solving large sparse linear systems. For many applications, it is not unusal that the order of matrix is greater than 10, 000. For this kind of problem, a direct method such as Gaussian elimination can not be used because of the prohibitive cost. To this end, many iterative methods with modest cost have been studied and proposed by numerical analysts.(omitted)

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.303-306
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• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

• Kyungpook Mathematical Journal
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• v.50 no.1
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• pp.7-14
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• 2010

In this paper, we construct some improvements of the Jarratt method for solving non-linear equations. A new sixth-order method are developed and numerical examples are given to support that the method obtained can compete with other sixth-order iterative methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.385-392
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• 2002

This paper introduces a new approach to analysis of error convergence for a class of iterative teaming control systems. Firstly, a nonlinear plant is represented using a Takagi-Sugeno(T-S) fuzzy model. Then each iterative learning controller is designed for each linear plant in the T-S fuzzy model. From the view point of two-dimensional(2-D) system theory, we transform the proposed learning systems to a 2-D error equation, which is also established if the form of T-S fuzzy model. We analyze the error convergence in the sense of induced L$_2$-norm, where the effects of disturbances and initial conditions on 2-D error are considered. The iterative teaming controller design problem to guarantee the error convergence can be reduced to the linear matrix inequality problem. This method provides a systematic design procedure for iterative teaming controller. A simulation example is given to illustrate the validity of the proposed method.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.62-67
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• 2009

The demand for high performance computer grows to solve large linear systems of equations in such engineering fields - circuit simulation for VLSI design, image processing, structural engineering, aerodynamics, etc. Many various parallel processing systems have been proposed and manufactured to satisfy the demand. The properties of linear system determine what algorithm is proper to solve the problem. Direct methods or iterative methods can be used for solving the problem. In this paper, an intelligent parallel iterative method for solving linear systems of equations with large sparse matrices is proposed and its efficiency is proved through simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.323-330
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• 2019

Radiometric normalization with multi-temporal satellite images is essential for time series analysis and change detection. Generally, relative radiometric normalization, which is an image-based method, is performed, and histogram matching is a representative method for normalizing the non-linear properties. However, since it utilizes global statistical information only, local information is not considered at all. Thus, this paper proposes a histogram matching method considering local information. The proposed method divides histograms based on density, mean, and standard deviation of image intensities, and performs histogram matching locally on the sub-histogram. The matched histogram is then further partitioned and this process is performed again, iteratively, controlled with the wasserstein distance. Finally, the proposed method is compared to global histogram matching. The experimental results show that the proposed method is visually and quantitatively superior to the conventional method, which indicates the applicability of the proposed method to the radiometric normalization of multi-temporal images with non-linear properties.

• Journal of the Korean Society of Propulsion Engineers
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• v.5 no.1
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• pp.69-75
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• 2001

Linear GPA(Gas Path Analysis) and non-linear GPA programs for performance diagnostics of a turboprop engine were developed, and a study for selection of optimal measurement variables was performed. Simultaneous faults in the compressor, the compressor turbine and the power turbine, which occur damage of the engine, were assumed. The non-linear GPA analysis was carried out with an iterative method, where the performance degradation rate of independent parameters was divided into same intervals. It was compared with the result by the Newton-Raphson method for observing the effect of an iterative method. According to the analysis result, it was found that performance of non-linear GPA can be influenced on the type of the iterative method. For showing effects of the number of measurement variables both the linear and non-linear GPAs were analyzed with 10, 8 and 6 measurement sets, respectively. RMS error between them were compared each other. It was realized that the more measurement parameters are used, and the more accurate result may be obtained. However much better result can be obtained with measurement parameters selected properly Moreover, RMS error by using non-linear GPA was less than that by using linear GPA.

• Bulletin of the Korean Mathematical Society
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• v.60 no.5
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• pp.1181-1199
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• 2023

For solving complex symmetric positive definite linear systems, we propose a single step real-valued (SSR) iterative method, which does not involve the complex arithmetic. The upper bound on the spectral radius of the iteration matrix of the SSR method is given and its convergence properties are analyzed. In addition, the quasi-optimal parameter which minimizes the upper bound for the spectral radius of the proposed method is computed. Finally, numerical experiments are given to demonstrate the effectiveness and robustness of the propose methods.