• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.526-531
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• 1997

To control nonlinear chemical processes, a new neural linearizing control scheme is proposed. This is a hybrid of a radial basis function(RBF) network and a linear controller, thus the control action applied to the process is the sum of both control actions. Firstly, to train the RBF newtork a linear reference model is determined by analyzing the past operating data of the process. Then, the training of the RBF newtork is iteratively performed to minimize the difference between outputs of the process and the linear reference model. As a result, the apparent dynamics of the process added by the RBF newtork becomes similar to that of the linear reference model. After training, the original nonlinear control problem changes to a linear one, and the closed-loop control performance is improved by using the optimum tuning parameters of the linear controller for the linear dynamics. The proposed control scheme performs control and training simultaneously, and shows a good control performance for nonlinear chemical processes.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.10
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• pp.127-136
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• 2004

This paper is the first of two companion papers concerning the analysis and design of a pneumatic vibration isolation system. The design optimization of the pneumatic vibration isolation system is required for the reduction of cost, endeavor and time, and it needs modeling and calculation algorithm. The nonlinear models are devised from the fluid mechanical expression for components of the system and the calculation algorithm is derived from the mathematical relationship between the models. It is shown that the orifice makes the nonlinear property of the transmissibility curve that the resonant frequency changes by the amplitude of excited vibration. Linearization of the nonlinear models is tried to reduce elapsed time and truncation error accumulation and to enable the transmissibility calculation of the system with multi damping chambers. The equivalent mechanical models generated by linearization clarify the function of each component of the system and lead to the linearized transfer function that can give forth to the transmissibility exactly close to that of nonlinear models. The modified successive under-relaxation method is developed to calculate the linearized transfer function.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.452-460
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• 2003

In this paper, an equalizer(RNE) using nonlinear multilayer combining techniques in Bayesian equalizer with a structure of radial basis function network is proposed in order to simplify the structure and enhance the performance of the equalizer(RE) using a radial basis function network. The conventional RE Produces its output using linear combining the outputs of the basis functions in the hidden layer while the proposed RNE produces its output using nonlinear combining the outputs of the basis function in the first hidden layer. The nonlinear combiner is implemented by multilayer perceptrons(MLPs). In addition, as an infinite impulse response structure, the RNE with decision feedback equalizer (RNDFE) is proposed. The proposed equalizer has simpler structure and shows better performance than the conventional RE in terms of bit error probability and mean square error.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.420-425
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• 2001

The information content is determined by establishing the system range for each of the FRs and by determing the overlap between system range and the design range (i.e the designer-specified range). However, conventional information content doesn't include designer's intention sufficiently. In this paper, the satisfaction function is presented to embody designer's intention by calculating information contents. The satisfaction function is created in order to deal with the uncertanties involved in determining the design range and the system range in terms of a given physical parameter. So, the satisfaction function help designer to choose the optimal design among many proposed design.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.315-318
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• 2003

This paper proposes a switching control method applicable to any affine, 2nd order nonlinear system with single input. The key contribution is to develop a control design method which uses a piecewise continuous Lyapunov function non-increasing at every discontinuous point. The proposed design method requires no restrictions except full state availability. To obtain a non-increasing, piecewise continuous Lyapunov function, we change the sign of off-diagonal term s of the positive definite matrix composing the former Lyapunov function according to the sign of the Inter-connection term. And we use the solution of inequalities which guarantee each Lyapunov function is non-increasing at any discontinuous point.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1177-1180
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• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1533-1539
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• 2011

In this paper, a class of nonlinear bilevel programs, i.e. the lower level problem is linear programs, is considered. Aiming at this special structure, we append the duality gap of the lower level problem to the upper level objective with a penalty and obtain a penalized problem. Using the penalty method, we give an existence theorem of solution and propose an algorithm. Then, a numerical example is given to illustrate the algorithm.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.335-341
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• 2009

In this paper, a system of nonsmooth equations reformulated from a nonlinear complementarity problem with nonsmooth data is studied. The formulas of some subdifferentials for related functions in this system of nonsmooth equations are developed. The present work can be applied to Newton methods for solving this kind of nonlinear complementarity problem.

• Bulletin of the Korean Mathematical Society
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• v.33 no.3
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• pp.377-383
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• 1996

We consider in this paper the following nonlinear regression model $$ (1.1) y_t = f(x_t, \theta_o) + \in_t, t = 1, \ldots, n, $$ where $y_t$ is the tth response, $x_t$ is m-vector imput variable, $\theta_o$ is a p-vector of unknown parameter belong to a parameter space $\Theta, f:R^m \times \Theta \ to R^1$ is a nonlinear known function, and $\in_t$ are independent unobservable random errors with finite second moment.