• Journal of applied mathematics & informatics
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• v.30 no.3_4
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• pp.395-411
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• 2012

Several methods with order higher than that of Newton methods which are of order 2 have been reported in literature for solving nonlinear equations. The focus of most of these methods was to economize on/minimize the number of function evaluations per iterations. We have demonstrated here that there are several computational pit-falls, such as the violation of fixed-point theorem, that one could encounter while using these methods. Further it was also shown that the overall computational complexity could be more in these high-order methods than that in the second-order Newton method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.99-102
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• 1996

A novel iterative learning control scheme comprising a unique feedforward learning controller and a disturbance observer is proposed. Disturbance observer compensates disturbance due to parameter variations, mechanical nonlinearities, unmodeled dynamics and external disturbances. The convergence and robustness of the proposed controller is proved by the method based on Lyapunov stability theorem. The results of numerical simulation are shown to verify the effectiveness of the proposed control scheme.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.621-629
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• 2007

In the adaptive fuzzy control field for affine nonlinear systems, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In general, these restrictions are difficult to check in practice where mathematical models of plant are not available. In this paper, using a simple extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. The Lyapunov synthesis approach is used to guarantee the stability and convergence of the closed loop system. Finally, examples of an inverted pendulum and a magnet levitation system demonstrate the theoretical results.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.545-552
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• 2003

Let X$_1$, X$_2$, … be real valued random variables under linearly negatively quadrant dependent (LNQD). In this paper, we discuss the probability inequality of ennett(1962) and Hoeffding(1963) under some suitable random variables. These results are to extend Theorem A and B to LNQD random variables. Furthermore, let ζdenote the ｐth quantile of the marginal distribution function of the $Ｘ_i$'s which is estimated by a smooth estima te $ζ_{pn}$, on the basis of X$_1$, X$_2$, …$X_n$. We establish a convergence of $ζ_{pn}$, under Hoeffding-type probability inequality of LNQD.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1992.03a
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• pp.159-176
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• 1992

Limit analysis has been rendered versatile in many problems such as structural problems and metal forming problems. In metal forming analysis, a slip-line method and an upper bound method approach to limit solutions is considered as the most challenging areas. In the present work, a general algorithm for limit solutions of plastic flow is developed with the use of finite element limit analysis. The algorithm deals with a generalized Holder inequality, a duality theorem, and a combined smoothing and successive approximation in addition to a general procedure for finite element analysis. The algorithm is robust such that from any initial trial solution, the first iteration falls into a convex set which contains the exact solution(s) of the problem. The idea of the algorithm for limit solution is extended from rigid/perfectly-plastic materials to work-hardening materials by the nature of the limit formulation, which is also robust with numerically stable convergence and highly efficient computing time.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.233-240
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• 2006

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.99-106
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• 2003

A new reconfigurable model following flight control method based on direct adaptive scheme is presented. Using the timescale separation principle, both the inner-loop and the outer-loop states are controlled simultaneously. For the timescale separation assumption to be satisfied, the inner-loop model dynamics is set to be fast whereas the outer-loop model dynamics is set to be relatively slow. The stability and convergence of the proposed control law is proved by Lyapunov theorem. One of the merits of the proposed reconfigurable controller is that the FDI process and the persistent input excitation are not necessary, which is suitable for the flight control system. To evaluate the reconfiguration performance of the proposed control method, numerical simulation is performed using six degree-of-freedom nonlinear dynamics.

• Journal of applied mathematics & informatics
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• v.28 no.1_2
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• pp.87-98
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• 2010

The purpose of this paper is to prove a weak convergence theorem for a common fixed points of finite family of nonspreading mappings and nonexpansive mappings in Hilbert spaces. The results presented in this paper extend and improve the results of Mondafi [A. Moudafi, Krasnoselski-Mann iteration for hierarchical fixed-point problems, Inverse Problems 23 (2007) 1635-1640], and Iemoto and Takahashi [So Iemoto, W.Takahashi, Approximating common fixed points of nonexpansive mappings and nonspreading mappings in a Hilbert space, Nonlinear Analysis (2009), doi:10.1016/j.na.2009.03.064].

• Bulletin of the Korean Mathematical Society
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• v.48 no.4
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• pp.779-789
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• 2011

In this paper we study the Banach space $L^1$(G) of real valued measurable functions which are integrable with respect to a vector measure G in the sense of D. R. Lewis. First, we investigate conditions for a scalarly integrable function f which guarantee $f{\in}L^1$(G). Next, we give a sufficient condition for a sequence to converge in $L^1$(G). Moreover, for two vector measures F and G with values in the same Banach space, when F can be written as the integral of a function $f{\in}L^1$(G), we show that certain properties of G are inherited to F; for instance, relative compactness or convexity of the range of vector measure. Finally, we give some examples of $L^1$(G) related to the approximation property.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.855-868
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• 1998

For a random sample of size n from general linear model, $Y_i= heta(X_i)+varepsilon_i,;let Y_{in}$ denote the ith oder statistics of the Y sample values. The X-value associated with $Y_{in}$ is denoted by $X_{[in]}$ and is called the concomitant of ith order statistics. The estimator of the location of a maximum of a regression function, $ heta$($\chi$), was proposed by (equation omitted) and was found the convergence rate of it under certain weak assumptions on $ heta$. We will discuss the asymptotic distributions of both $ heta(X_{〔n-r+1〕}$) and (equation omitted) when r is fixed as nolongrightarrow$\infty$(i.e. extreme case) on the basis of the theorem of the concomitants of order statistics. And the will investigate the asymptotic behavior of Max｛$\theta$( $X_{〔n-r+1:n〕/}$ ), . , $\theta$( $X_{〔n:n〕}$)｝as an estimator for the peak of a regression function.