• 대한기계학회논문집A
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• 제33권8호
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• pp.753-759
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• 2009

Feed-forward neural networks have been widely used as function approximation tools in the context of global approximate optimization. In the present study, a wavelet neural network (WNN) which is based on wavelet transform theory is suggested as an alternative to a traditional back-propagation neural network (BPN). The basic theory of wavelet neural network is briefly described, and approximation performance is tested using a nonlinear multimodal function and a composite rotor blade analysis problem. Laplacian of Gaussian function, Mexican function, and Morlet function are considered during the construction of WNN architectures. In addition, approximation results from WNN are compared with those from BPN.

• 대한수학회보
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• 제44권1호
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• pp.125-130
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• 2007

We propose a simple and intuitive method to derive the exact convergence rate of global $L_{2}-norm$ error for strong numerical approximation of stochastic differential equations the result of which has been reported by Hofmann and $M{\"u}ller-Gronbach\;(2004)$. We conclude that any strong numerical scheme of order ${\gamma}\;>\;1/2$ has the same optimal convergence rate for this error. The method clearly reveals the structure of global $L_{2}-norm$ error and is similarly applicable for evaluating the convergence rate of global uniform approximations.

• 전자공학회논문지B
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• 제31B권4호
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• pp.145-154
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• 1994

This paper proposes an efficient method for improving the training performance of the neural network by using a hybrid of a stochastic approximation and a backpropagation algorithm. The proposed method improves the performance of the training by appliying a global optimization method which is a hybrid of a stochastic approximation and a backpropagation algorithm. The approximate initial point for a stochastic approximation and a backpropagation algorihtm. The approximate initial point for fast global optimization is estimated first by applying the stochastic approximation, and then the backpropagation algorithm, which is the fast gradient descent method, is applied for a high speed global optimization. And further speed-up of training is made possible by adjusting the training parameters of each of the output and the hidden layer adaptively to the standard deviation of the neuron output of each layer. The proposed method has been applied to the parity checking and the pattern classification, and the simulation results show that the performance of the proposed method is superior to that of the backpropagation, the Baba's MROM, and the Sun's method with randomized initial point settings. The results of adaptive adjusting of the training parameters show that the proposed method further improves the convergence speed about 20% in training.

• 대한기계학회논문집A
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• 제29권9호
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 대한수학회논문집
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• 제29권4호
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• pp.505-518
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• 2014

Considered here is the first initial boundary value problem for the two-dimensional g-Navier-Stokes equations in bounded domains. We first study the long-time behavior of strong solutions to the problem in term of the existence of a global attractor and global stability of a unique stationary solution. Then we study the long-time finite dimensional approximation of the strong solutions.

• Journal of Mechanical Science and Technology
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• 제15권8호
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• 한국전산구조공학회논문집
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• 제12권3호
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• pp.397-406
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• 1999

본 연구는 부구조화에 기초한 다단계 혼성 구조 재해석방법을 제시한다. 부구조화의 틀에 보존근사화의 각 항을 차원축소법의 기저로 한 보존 전역-부분근사화에 의하여 변위 산정의 정확성과 효율성을 확보하고, 이를 바탕으로 이미 구성된 응력-변위 관계식을 병용하는 혼성방식을 통하여 전체 설계의 중간 단계에서 반복되는 재해석 과정의 신뢰성을 높인다. 전체적으로 선형근사화와 상반근사화를 교차적용하는 1단계 보존근사화로부터 전역 근사화와 결합하여 구하는 변위산정과 그에 종속되는 행렬연산으로 산출하는 응력계산의 3단계로 이루어지는 본 방법은 대형 구조계를 대상으로 하여, 해석의 기본 틀로 부구조화 방법을 택하였으며, 몇 개의 예제들을 통하여 타당성 및 유용성을 검증하였다.

• 한국강구조학회 논문집
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• 제12권1호통권44호
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• pp.45-54
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• 2000

본 연구는 반복되는 중간단계의 구조설계과정에 연동되는 근사 재해석 모델을 비교 평가하고, 특히 설계변수가 크게 변화할 때도 일정한 정확도를 유지하여 전체 설계과정에 효율성과 신뢰성을 함께 줄 수 있는 안정된 모델을 찾는다. 이를 위해 대형 프레임구조를 대상으로 설계변수 그룹의 갯수와 변화량을 달리하면서 최대 변위값을 정해와 비교하여 정확도 및 신뢰성을 검토하고, CPU 연산시간 비교를 통해 효율성을 시험한다. 예제를 통하여 부분근사화는 가장 간편하고 빠르기는 하나 설계의 면화가 극히 적은 특정한 경우에만 유용한 반면 전역근사화는 기저벡터를 효과적으로 취할 경우 설계 변화량이 클 때에도 비교적 높은 정확도를 유지하나 효율성이 떨어지는 취약점을 갖고 있다. 이들에 비해 전역-부분근사화는 어느 경우에나 높은 정확성과 효율성을 아울러 갖추고 있음을 보여준다. 이 방법들을 구조 재설계 과정에 연계할 때 설계정보에 따라 혼용하므로써 효율성을 증대시킬 수 있다.

• 대한수학회지
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• 제43권2호
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• pp.425-443
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• 2006

The aim of this paper is to obtain several results in approximation by Jackson-type generalizations of complex Picard, Poisson-Cauchy and Gauss-Weierstrass singular integrals in terms of higher order moduli of smoothness. In addition, these generalized integrals preserve some sufficient conditions for starlikeness and univalence of analytic functions. Also approximation results for vector-valued functions defined on the unit disk are given.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 춘계학술대회 학술발표 논문집
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• pp.122-125
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• 2000

The paper describes the adaptation of evolutionary fuzzy model ins (EFM) in developing global function approximation tools for use in genetic algorithm based optimization of nonlinear structural systems. EFM is an optimization process to determine the fuzzy membership parameters for constructing global approximation model in a case where the training data are not sufficiently provided or uncertain information is included in design process. The paper presents the performance of EFM in terms of numbers of fuzzy rules and training data, and then explores the EFM based sizing of automotive component for passenger protection.