• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2319-2324
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• 2005

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on-line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control effort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the effectiveness of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.406-413
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• 2007

This paper proposes a self-recurrent wavelet neural network(SRWNN) based adaptive backstepping control technique for the robust steering control of autonomous underwater vehicles(AUVs) with unknown model uncertainties and external disturbance. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the steering model of AUV. The adaptation laws for the weights of SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for the on-line control of AUV. Finally, simulation results for steering control of an AUV with unknown model uncertainties and external disturbance are included to illustrate the effectiveness of the proposed method.

• East Asian mathematical journal
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• v.24 no.2
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• pp.191-199
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• 2008

In this paper, we introduce and studied a system of nonlinear projection equations with perturbation in Hilbert spaces. By using the fixed point theorem, we prove an existence of solution for this system of nonlinear projection equations. We construct an algorithm for approximating the solution of the system of nonlinear projection equations with perturbation and show that the iterative sequence generated by the algorithm converges to the solution of the system of nonlinear projection equations with perturbation under some suitable conditions.

• Steel and Composite Structures
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• v.22 no.1
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• pp.161-182
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• 2016

A state space differential reproducing kernel (DRK) method is developed for the three-dimensional (3D) analysis of functionally graded material (FGM) axisymmetric circular plates with simply-supported and clamped edges. The strong formulation of this 3D elasticity axisymmetric problem is derived on the basis of the Reissner mixed variational theorem (RMVT), which consists of the Euler-Lagrange equations of this problem and its associated boundary conditions. The primary field variables are naturally independent of the circumferential coordinate, then interpolated in the radial coordinate using the early proposed DRK interpolation functions, and finally the state space equations of this problem are obtained, which represent a system of ordinary differential equations in the thickness coordinate. The state space DRK solutions can then be obtained by means of the transfer matrix method. The accuracy and convergence of this method are examined by comparing their solutions with the accurate ones available in the literature.

• East Asian mathematical journal
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• v.26 no.5
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• pp.703-714
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• 2010

A new class of nonlinear set-valued mixed variational inclusions involving (A, ${\eta}$)-accretive mappings in Banach spaces is introduced and studied, which includes many kind of variational inclusion (inequality) and complementarity problems as special cases. By using the resolvent operator associated with (A, ${\eta}$)-accretive operator due to Lan-Cho-Verma, the existence of solution for this kind of variational inclusion is proved, and a new hybrid proximal point algorithm is established and suggested, the convergence and stability theorems of iterative sequences generated by new iterative algorithms are also given in q-uniformly smooth Banach spaces.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.43-55
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• 2005

In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.134-137
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• 2000

Genetic algorithms(GAs) have been widely used as a method to solve optimization problems. This is because GAs have simple and elegant tools with reproduction, crossover, and mutation to rapidly discover good solutions for difficult high-dimensional problems. They, however, do not guarantee the convergence of global optima in GA-hard problems such as deceptive problems. Therefore we proposed a Schema Co-Evolutionary Algorithm(SCEA) and derived extended schema 76988theorem from it. Using co-evolution between the first population made up of the candidates of solution and the second population consisting of a set of schemata, the SCEA works better and converges on global optima more rapidly than GAs. In this paper, we show advantages and efficiency of the SCEA by applying it to some problems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.553-559
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• 1998

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA goes well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.1
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• pp.22-28
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• 1989

This paper describes error estimate mothod for adaptive finite element analysis of two dimensional electrostatic and magnetostatic field problems. To estimate the local errors, divergence theorem is used for electrostatic field and Ampere's circuital law for magnetostatic field. To confirm the effectiveness of the proposed error estimators, adaptive finite element computations are performed using the proposed error estimators. The rates of convergence of global errors are comparable with those of existing adaptive finite element schemes which make use of field continuity conditions between element boundaries. This algorithm of error estimate can be easily implemented because of its simplicity. Especially, when the value of charge in electrostatic field and the value of current in magnetostatic field are to be figured out, this method is considerded to be preferable to other approaches.

• Journal for History of Mathematics
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• v.19 no.2
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• pp.115-124
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• 2006

Functions of bounded variation were discovered by Jordan in 1881 while working out the proof of Dirichlet concerning the convergence of Fourier series. Here, we investigate Waterman's study with respect to bounded variation and its application on a closed bounded interval. The value of his study is whether Dirichlet-Jordan theorem holds in which function classes or not and summability method is what modifies its Fourier coefficients to make resulting series converge to the associated function. We have a view that the directions of future research with respect to bounded variation are two things; one is to find the function spaces which are larger than HBV and smaller than ${\phi}BV$, and the other is to find a fields of applications.