• International Journal of Control, Automation, and Systems
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• 제2권4호
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• pp.523-529
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• 2004

Sliding mode control with nonlinear interpolation in the boundary layer is proposed. A modified sigmoid function is used for nonlinear interpolation in the boundary layer and its parameter is tuned by a fuzzy controller. The fuzzy controller that takes both the sliding variable and a measure of chattering as its inputs tunes the parameter of the modified sigmoid function. Owing to the decreased thickness of the boundary layer and the tuned parameter, the proposed method has superior tracking performance than the conventional linear interpolation method.

• 충청수학회지
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• 제30권2호
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• pp.227-238
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• 2017

The existence of solutions for nonlinear elliptic partial differential equations with general flux and damping terms is investigated.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2002년도 ICCAS
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• pp.61.5-61
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• 2002

1. Introduction 2. Development of the Proposed Identification Method 2.1 MlMO Hammerstein nonlinear process 2.2 Process activation 2.3 Identification of the linear dynamic subsystem 2.4 Identification of the nonlinear static function 3. Simulation Study 4. Conclusion. Acknowledgment. References

• Journal of applied mathematics & informatics
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• 제4권1호
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• pp.47-62
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• 1997

This paper is concerned with investigating the global as-ymptotic behavior of the zero solution of the initial-boundary value problem for a nonlinear fourth order wave equation, Moreover an esti-mate of the rate of decay of the solutions is obtained.

• Kyungpook Mathematical Journal
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• 제55권3호
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• pp.653-666
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• 2015

With the notion of weighted sharing of values we study the uniqueness of meromorphic functions when certain nonlinear differential polynomials share a nonzero polynomial. Our results improve some recent results including that of a present first author.

• East Asian mathematical journal
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• 제16권2호
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• pp.317-330
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• 2000

In this paper we consider the optimal control problem of both operators and parameters for nonlinear hyperbolic systems. For the identification problem, we show that for every value of the parameter and operators, the optimal control problem has a solution. Moreover we obtain the necessary conditions of optimality for the optimal control problem on the system.

• Journal of the Optical Society of Korea
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• 제6권3호
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• pp.96-99
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• 2002

We give a brief overview of nonlinear localized modes in photonic crystals. We explain how photonic crystals can potentially be important in making small scale active devices which operate in an all optical way. Two models to approach nonlinear photonic crystals, the coupled mode theory and the discrete lattice theory using a Green's function, are explained.

• ETRI Journal
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• 제15권2호
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.

• Structural Engineering and Mechanics
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• 제20권5호
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• pp.575-587
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• 2005

Using the nonlinear load transfer function for pile side soil and the linear load transfer function for pile end soil, a combined approach of the incremental load transfer matrix method and the approximate differential equation solution method is presented for the nonlinear analysis of interaction between flexible pile group and soil. The proposed method provides an effective approach for the solution of the nonlinear interaction between flexible pile group under rigid platform and surrounding soil. To verify the accuracy of the proposed method, a static load test for a nine-pile group under a rigid platform is carried out. The finite element analysis is also conducted for comparison purposes. It is found that the results from the proposed method match very well with those from the experimental test and are better in comparison with the finite element method.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.189-199
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• 1999

This paper presents an optimization algorithm for a stable Self Dynamic Neural Network(SDNN) using genetic algorithm. Optimized SDNN is applied to a problem of controlling nonlinear dynamical systems. SDNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real-time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDW has considerably fewer weights than DNN. Since there is no interlink among the hidden layer. The object of proposed algorithm is that the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling a nonlinear dynamic system using the proposed optimized SDNN considering stability is demonstrated by case studies.