• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.45-49
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• 2000

A GPS measurement solution, in general, is obtained as a least squares solution since the measurement includes errors such as clock errors, ionospheric and tropospheric delays, multipath effect etc. Because of the nonlinearity of the measurement equation, we utilize the nonlinear Newton algorithm to obtain a least squares solution, or mostly, use its linearized algorithm which is more convenient and effective. In this study we developed a fixed point algorithm and proved its availability to replace the nonlinear Newton algorithm and the linearized algorithm. A nonlinear Newton algorithm and a linearized algorithm have the advantage of fast convergence, while their initial values have to be near the unknown solution. On the contrary, the fixed point algorithm provides more reliable but slower convergence even if the initial values are quite far from the solution. Therefore, two types of algorithms may be combined to achieve better performance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.3
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• pp.127-133
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• 2002

This paper presents an efficient improvement of the iterative eigenvalue calculation method of the AESOPS algorithm. The intuitively and heuristically approximated iterative eigenvalue calculation method of the AESOPS algorithm is transformed to the Second Order Newton Raphson Method which is generally used in numerical analysis. The equations of second order partial differentiation of external torque, terminal and internal voltages are derived from the original AESOPS algorithm. Therefore only a few calculation steps are added to transform the intuitively and heuristically approximated AESOPS algorithm to the Second Order Newton Raphson Method, while the merits of original algorithm are still preserved.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.435-439
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• 2007

This paper deals with RLS algorithm using Newton-Raphson method based adaptive forgetting factor for a passive telemetry RF sensor system in order to estimate the time variant parameter to be included in RF sensor model. For this estimation with RLS algorithm, phasor typed RF sensor system modelled with inductive coupling principle is used. Instead of applying constant forgetting factor to estimate time variant parameter, the adaptive forgetting factor based on Newton-Raphson method is applied to RLS algorithm without constant forgetting factor to be determined intuitively. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.47-48
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• 2012

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.43 no.3 s.345
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• pp.15-20
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• 2006

A Newton-Raphson Method for table driven algorithm is presented in this paper. We concentrate the approximation of square root by using Newton-Raphson method. We confirm that this method has advantages of accurate and fast processing with optimized initial point. Hence the selection of the fitted initial points used in approximation of Newton-Raphson algorithm is important issue. This paper proposes that log scale based on geometric wean is most profitable initial point. It shows that the proposed method givemore accurate results with faster processing speed.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.285-292
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• 2014

The commonly used Newton-Raphson's floating-point number divider algorithm performs two multiplications in one iteration. In this paper, a tentative K'th Newton-Raphson's floating-point number divider algorithm which performs K times multiplications in one iteration is proposed. Since the number of multiplications performed by the proposed algorithm is dependent on the input values, the average number of multiplications per an operation in single precision and double precision divider is derived from many reciprocal tables with varying sizes. In addition, an error correction algorithm, which consists of one multiplication and a decision, to get exact result in divider is proposed. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a floating point number divider unit. Also, it can be used to construct optimized approximate reciprocal tables.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.1
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• pp.45-51
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• 2018

In this paper, a tentative Kth order Newton-Raphson's floating point number Nth root algorithm for K order convergence rate in one iteration is proposed by applying Taylor series to the Newton-Raphson root algorithm. Using the proposed algorithm, $F^{-1/N}$ and $F^{-(N-1)/N}$ can be computed from iterative multiplications without division. It also predicts the error of the algorithm iteration and iterates only until the predicted error becomes smaller than the specified value. Since the proposed algorithm only performs the multiplications until the error gets smaller than a given value, it can be used to improve the performance of a floating point number Nth root unit.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.909-920
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• 2011

A regularized Newton method for nonlinear ill-posed problems is considered. In each Newton step an implicit iterative method with an appropriate stopping rule is proposed and analyzed. Under certain assumptions on the nonlinear operator, the convergence of the algorithm is proved and the algorithm is stable if the discrepancy principle is used to terminate the outer iteration. Numerical experiment shows the effectiveness of the method.

• Journal of the Korean Mathematical Society
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• v.55 no.6
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• pp.1529-1540
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• 2018

The matrix equation $X^p+A^*XA=Q$ has been studied to find the positive definite solution in several researches. In this paper, we consider fixed-point iteration and Newton's method for finding the matrix p-th root. From these two considerations, we will use the Newton-Schulz algorithm (N.S.A). We will show the residual relation and the local convergence of the fixed-point iteration. The local convergence guarantees the convergence of N.S.A. We also show numerical experiments and easily check that the N.S. algorithm reduce the CPU-time significantly.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.20 no.6
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• pp.731-742
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• 2007

This paper presents a new nonlinear analysis algorithm, that is, adaptive Newton-Raphson iteration method, The presented algorithm is based on the existing Newton-Raphson method, and the concept of it can be summarized as calculating the equivalent load for stiffness(ELS) and adapting this to the initial global stiffness matrix which has already been calculated and saved in initial analysis and finally calculating the correction displacements for the nonlinear analysis, The key characteristics of the proposed algorithm is that it calculates the inverse matrix of the global stiffness matrix only once irresponsive of the number of load steps. The efficiency of the proposed algorithm depends on the ratio of the active Dofs - the Dofs which are directly connected to the members of which the element stiffness are changed - to the total Dofs, and based on this ratio by using the proposed algorithm as a complementary method to the existing algorithm the efficiency of the nonlinear analysis can be improved dramatically.