• 전자공학회논문지T
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• 제35T권3호
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• pp.87-95
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• 1998

본 논문에서는 시스템의 차수가 고차이며 잡음과 시간지연이 있고 시스템의 파라미터가 변하는 비최소위상 시스템에 적응할 수 있는 자기동조 제어기의 설계 하중다항식 계수를 온라인으로 조정하는 방법을 제안한다. 일반화 최소분산 자기동조 제어기의 설계 하중다항식 계수 값은 확률 근사법인 Robbins-Monro알고리즘을 이용하여 온라인으로 얻으며 자기동조 제어기의 파라미터는 순환최소자승법으로 추정하였다. 제안한 자기동조 제어 방법은 다른 자기동조 제어 방법들11.21보다 간단하고 효과적이다. 제어 알고리즘의 타당성을 확인하기 위해 일정한 시간이 경과한 후 시스템의 파라미터가 변하고 시스템의 영점이 단위원 밖에 있는 고차 시스템에 대해 컴퓨터 시뮬레이션을 하였다.

• Journal of information and communication convergence engineering
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• 제2권3호
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• pp.193-197
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• 2004

In this paper, a combination problem of controllers which are the same type of $H_{\infty}$ controllers designed with different weighting functions. This approach can remove the difficulty in the selection of the weighting functions. As a sub-controller, the Youla type of $H_{\infty}$ controller is used. In the $H_{\infty}$ controller, Youla parameterization is used to minimize $H_{\infty}$ norm of mixed sensitivity function by using polynomial approach. Computer simulation results show the robustness improvement and the performance improvement.

• 전자공학회논문지S
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• 제36S권11호
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• pp.22-33
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• 1999

본 논문에서는 시스템의 차수가 고차이고 잡음과 시간지연이 있으며 파라미터가 변하는 비최소위상 시스템에 적응할 수 있는 다변수 일반화 자기동조 제어기의 설계 하중다항식 계수들을 온-라인으로 조정하는 방법을 제안한다. 다변수 일반화 최소분산 자기동조 제어기의 파라미터는 순환최소자승법으로 추정하고 설계 하중다항식 계수들의 값은 확률근사법인 Robbins-Monro알고리듬을 이용하여 자동 조절하였다. 제안한 다변수 자기동조 방법은 극제한방법보다 간단하고 효과적이다. 컴퓨터 시뮬레이션을 통해 제안한 방법이 시스템의 파라미터가 변하고 시스템의 영점이 단위원 밖에 있는 고차 다변수 시스템에 잘 적응함을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.629-632
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• 1997

This paper presents the design method of controller which is combined Genetic Algorithms with the Generalized minimum variance self tuning controller. It is shown that the controllers adapts to changes in the system parameters with time delays and noises. The self tuning effect is achieved through the recursive least square algorithm at the parameter estimation stage and also through the Robbins-Monro algorithm at the stage of optimizing a polynomial parameters. The computer simulation results are presented to illustrate the procedure and to show the performance of the control system.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1993년도 춘계공동학술대회 발표논문 및 초록집; 계명대학교, 대구; 30 Apr.-1 May 1993
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• pp.6-15
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• 1993

A generalization of one machine maximum lateness minimization problem is considered. There are one achine with controllable speed and n weighting jobs $J_{j}$, j=1, 2, ..., n with ambiguous duedates. Introducing fuzzy formulation, a membership function of the duedate associated with each job $J_{j}$, which describes the satisfaction level with respect to completion time of $J_{j}$. Thus the duedates are not constants as in conventional scheduling problems but decision variables reflecting the fuzzy circumstance of the job completing. We develop the polynomial time algorithm to find an optimal schedule and jobwise machine speeds, and to minimize the total sum of costs associated with jobwise machine speeds and dissatisfaction with respect to completion times of weighting jobs. jobs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.429-434
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• 1990

Given the polynomial in z, P$_{0}$ (z) = z$^{n}$ + a$_{1}$z$^{n-1}$ + a$_{2}$z$^{n-2}$ + ... + a$_{n-1}$z + a$_{0}$ , it is of interest to know how much coefficient a$_{I}$ can be perturbed while simultaneously preserving the stable property of the polynomials. In this paper, we derive the maximal intervals, centered about the nominal values of the coefficients, having the following property: the polynomial remains stable for all variations within these intervals. And then, under the unfixed weighted perturbation evaluate upper and lower allowable perturbations.tions.s.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.936-938
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• 1996

The $H_{\infty}$ robust controller is designed for on-line adaptive control application by using polynomial approach. The $H_{\infty}$ robust controllers for adaptive system were designed first by Grimble. But they have a problem that two minimum costs can exist and did not minimize the conventional $H_{\infty}$ cost function which is the $H_{\infty}$ sum of weighted sensitivity and complementary sensitivity terms. In this paper, the two minimum costs problem can be avoided and the conventional $H_{\infty}$ cost function is minimized by employing the Youla parameterization and polynomial approach at the same time. In addition pole placement is possible without any relation with weighting function.

• 한국정밀공학회지
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• 제23권8호
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• pp.89-99
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• 2006

In this paper a new shape reconstruction method that allows us to construct surface models from very large sets of points is presented. In this method the global domain of interest is divided into smaller domains where the problem can be solved locally. These local solutions of subdivided domains are blended together according to weighting coefficients to obtain a global solution using partition of unity function. The suggested approach gives us considerable flexibility in the choice of local shape functions which depend on the local shape complexity and desired accuracy. At each domain, a quadratic polynomial function is created that fits the points in the domain. If the approximation is not accurate enough, other higher order functions including cubic polynomial function and RBF(Radial Basis Function) are used. This adaptive selection of local shape functions offers robust and efficient solution to a great variety of shape reconstruction problems.

• 대한전자공학회논문지SP
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• 제48권3호
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• pp.119-124
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• 2011

보간 필터는 통신과 멀티미디어 응용프로그램에 널리 사용된다. 다항식 보간은 보간된 값을 얻기 위해서 입력정보에 따른 다항식의 계수를 계산한다. 최근에 다항식 보간방법의 성능을 향상시키기 위하여 보조필터를 이용한 FIR 보간방법이 제안되었다. 본 논문에서는 최대값 또는, 최소값 계산 등 특정 순간의 보간 값만 필요한 응용에서 보간필터의 성능을 더욱 향상시킬 수 있는 보조필터를 이용한 가중치 적용 보간방법을 제안한다. 제안한 방법을 X-선 형광분석장치에서 사용되는 정규분포곡선에 대한 보간에 적용하여 기존 보간방법에 비해 더욱 우수한 보간 성능이 제공됨을 보인다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권1호
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.