• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.63-68
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• 1998

This paper proposes fuzzy regression analysis with non-symmetric fuzzy coefficients. By assuming non-symmetric triangular fuzzy coefficients and applying the quadratic programming fomulation, the center of the obtained fuzzy regression model attains more central tendency compared to the one with symmetric triangular fuzzy coefficients. For a data set composed of crisp inputs-fuzzy outputs, two approximation models called an upper approximation model and a lower approximation model are considered as the regression models. Thus, we also propose an integrated quadratic programming problem by which the upper approximation model always includes the lower approximation model at any threshold level under the assumption of the same centers in the two approximation models. Sensitivities of Weight coefficients in the proposed quadratic programming approaches are investigated through real data.

• Management Science and Financial Engineering
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• 제21권2호
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• pp.25-30
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• 2015

This short article compares different solution methods for a basic RBC model (Hansen, 1985). We solve and simulate the model using two main algorithms: the methods of perturbation and projection, respectively. One novelty is that we offer a type of the hybrid method: we compute easily a second-order approximation to decision rules and use that approximation as an initial guess for finding Chebyshev polynomials. We also find that the second-order perturbation method is most competitive in terms of accuracy for standard RBC model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.81-98
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• 2013

Since Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) were introduced, many revised or extended topic models have appeared. Due to the intractable likelihood of these models, training any topic model requires to use some approximation algorithm such as variational approximation, Laplace approximation, or Markov chain Monte Carlo (MCMC). Although these approximation algorithms perform well, training a topic model is still computationally expensive given the large amount of data it requires. In this paper, we propose a new method, called non-simultaneous sampling deactivation, for efficient approximation of parameters in a topic model. While each random variable is normally sampled or obtained by a single predefined burn-in period in the traditional approximation algorithms, our new method is based on the observation that the random variable nodes in one topic model have all different periods of convergence. During the iterative approximation process, the proposed method allows each random variable node to be terminated or deactivated when it is converged. Therefore, compared to the traditional approximation ways in which usually every node is deactivated concurrently, the proposed method achieves the inference efficiency in terms of time and memory. We do not propose a new approximation algorithm, but a new process applicable to the existing approximation algorithms. Through experiments, we show the time and memory efficiency of the method, and discuss about the tradeoff between the efficiency of the approximation process and the parameter consistency.

• Journal of Radiation Protection and Research
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• 제15권2호
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• pp.51-56
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• 1990

대기로 방출된 방사성 물질의 대기 확산 형태를 파스킬의 대기안정도에 따른 모델인 타원형 근사화 모델로 가정하고 인체가 받을 수 있는 감마선에 의한 피폭선량률을 계산하였다. 이 결과를 대기 확산 기본 모델인 가우스플룸 모델을 적용하여 계산한 결과 및 이미 발표된 원형 근사화 모델에 의한 결과와 비교하여 보았다. 제시한 타원형 근사화 모델을 이용하여 피폭선량을 계산한 결과는 가우스플룸 모델의 결과와 비슷하고, 원형 근사화 모델의 경우보다 오차가 적었으며, 동시에 기본 모델인 가우스 플룸 모델과 비교할 때 1/40 정도의 계산 시간이 걸렸다.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• 제27권3호
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• pp.180-193
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• 2023

Approximating the implied volatilities and estimating the model parameters are important topics in quantitative finance. This study proposes an approximation formula for short-maturity near-the-money implied volatilities in stochastic volatility models. A general second-order nonlinear PDE for implied volatility is derived in terms of time-to-maturity and log-moneyness from the Feyman-Kac formula. Using regularity conditions and the Taylor expansion, an approximation formula for implied volatility is obtained for short-maturity nearthe-money call options in two stochastic volatility models: Heston model and SABR model. In addition, we proposed a novel numerical method to estimate model parameters. This method reduces the number of model parameters that should be estimated. Generating sample data on log-moneyness, time-to-maturity, and implied volatility, we estimate the model parameters fitting the sample data in the above two models. Our method provides parameter estimates that are close to true values.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.159-167
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• 2000

In optimization problems, computationally intensive or expensive simulations hinder the use of standard optimization techniques because the computational expense is too heavy to implement them at each iteration of the optimization algorithm. Therefore, those expensive simulations are often replaced with approximation models which can be evaluated nearly free. However, because of the limited accuracy of the approximation models, it is practically impossible to find an exact optimal point of the original problem. Significant efforts have been made to overcome this problem. The approximation models are sequentially updated during the iterative optimization process such that interesting design points are included. The interesting points have a strong influence on making the approximation model capture an overall trend of the original function or improving the accuracy of the approximation in the vicinity of a minimizer. They are successively determined at each iteration by utilizing the predictive ability of the approximation model. This paper will focuses on those approaches and introduces various approximation methods.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.170-177
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• 1998

In this paper, a unified solution of Hankel norm approximation problem is proposed by $\delta$-operator. To derive the main result, all-pass property is derived from the inner and co-inner property in the $\delta$-domain. The solution of all-pass becomes an optimal Hankel norm approximation problem in .delta.-domain through LLFT(Low Linear Fractional Transformation) inserting feedback term $\phi(\gamma)$, which is a free design parameter, to hold the error bound desired against the variance between the original model and the solution of Hankel norm approximation problem. The proposed solution does not only cover continuous and discrete ones depending on sampling interval but also plays a key role in robust control and model reduction problem. The verification of the proposed solution is exemplified via simulation for the zero-order Hankel norm approximation problem and the model reduction problem applied to a 16th order MIMO system.

• 대한산업공학회지
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• 제42권2호
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• pp.86-95
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• 2016

We propose a new approach to the stochastic version of Lanchester model. Commonly used approach to stochastic Lanchester model is through the Markov-chain method. The Markov-chain approach, however, is not appropriate to high dimensional heterogeneous force case because of large computational cost. In this paper, we propose an approximation method of stochastic Lanchester model. By matching the first and the second moments, the distribution of each unit strength can be approximated with multivariate normal distribution. We evaluate an approximation of discrete Markov-chain model by measuring Kullback-Leibler divergence. We confirmed high accuracy of approximation method, and also the accuracy and low computational cost are maintained under high dimensional heterogeneous force case.

• Journal of the Korean Data and Information Science Society
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• 제15권4호
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• pp.965-971
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• 2004

In this paper we consider several nonparametric estimators for the mean residual life by using the partial moment approximation under the proportional hazard model. Also we compare the magnitude of mean square error of the proposed nonparametric estimators for mean residual life under the proportional hazard model.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(3)
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• pp.17-20
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• 2002

This paper proposes a new 75 fuzzy model approximation method which reduces error in nonlinear fuzzy model approximation over the V-type decision rules. Employing rational Bezier curves used in computer graphics to represent curves or surfaces, the proposed method approximates the decision rule by constructing a tractable linear equation in the highly non-linear fuzzy rule interval. This algorithm is applied to the self-adjusting air cushion for spinal cord injury patients to automatically distribute the patient's weight evenly and balanced to prevent decubitus. The simulation results indicate that the performance of the proposed method is bettor than that of the conventional TS Fuzzy model in terms of error and stability.