• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.09a
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• pp.387-395
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• 1995

롤(Roll)의 주물 가공 시스템의 성능(Performance)분석의 문제는 일반적으로 관련된 많은 요인들 때문에 매우 복잡하다. 롤 제조 시스템의 성능과 관련된 주요 요인으로서 주형(Moulding) 제작, 원재료의 용해, 후처리 및 가공 공정 의 요인들을 들 수 있다. 본 연구에서는 이러한 복잡한 롤의 주물 및 가공공 정상의 요인들로부터 롤 제조 시스템의 불량률을 평가하기 위하여 발견적인 방법인 GMDH(Group Method Data Handling)-Type 모델링 방법을 이용하 였다. 롤 주물 가공 시스템의 성능을 불량률로 두고 이에 주요 영향 요인들 의 입력 Data를 위하여 현장 자료로부터 상하한 값을 구하여, Hyper-Cube 프로그램을 이용하여 필요한 수의 Data를 보완하여 사용하였다. 시스템 성능 과 관련된 인자들을 2개식 가능한 조합을 하고 이들 각각의 조합들에 대하 여 6개항으로 된 예측식으로 회귀분석하고 일정 수준 이상의 결과들만을 다 음 단계의 자료로 사용하였다. GMDH 방법은 매 단계마다 영향이 적은 변 수조합을 제외시키므로 최종 해는 그 정확성이 매우 높다. 본 연구를 위하여 GMDH 알고리즘에 따라 계산할수 있는 전산 프로그램을 개발하여 사용하였 으며, 적용예를 롤 주물제조공정에 응용하여 보였다.. 분석된 자료에 의하면 예측 오차가 매우 적음을 보였다.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.1
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• pp.73-86
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• 2009

This paper discusses a method of determining the optimal design of a block. A horizontal layout of blocks is assumed in which transfer points are located at a side of the block. Each block has several transfer points (TPs) each of which is assigned to a group of adjacent bays and located at the center of the assigned group. The goal is to find the optimal size of a block and the optimal number of TPs while minimizing the total cost consisting of the fixed and operational cost of yard cranes (YCs), the operational cost of internal trucks, and the installation cost of TPs. Constraints on the maximum expected system time of trucks are imposed for the optimization. Formulas for estimating handling operation cycle times of a YC are derived analytically. Numerical experiments are conducted to illustrate optimal block designs for a given set of data.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.58-69
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• 1998

This paper introduces a novel neuro-fuzzy system based on the polynomial fuzzy neural network(PFNN) architecture. The PFNN consists of a set of if-then rules with appropriate membership functions whose parameters are optimized via a hybrid genetic algorithm. A polynomial neural network is employed in the defuzzification scheme to improve output performance and to select appropriate rules. A performance criterion for model selection, based on the Group Method of DAta Handling is defined to overcome the overfitting problem in the modeling procedure. The hybrid genetic optimization method, which combines a genetic algorithm and the Simplex method, is developed to increase performance even if the length of a chromosome is reduced. A novel coding scheme is presented to describe fuzzy systems for a dynamic search rang in th GA. For a performance assessment of the PFNN inference system, three well-known problems are used for comparison with other methods. The results of these comparisons show that the PFNN inference system outperforms the other methods while it exhibits exceptional robustness characteristics.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.3
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• pp.145-156
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• 2000

In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.3
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• pp.309-330
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• 2009

In this paper, we introduce the architecture of Genetic Algorithm(GA) based Feed-forward Polynomial Neural Networks(PNNs) and discuss a comprehensive design methodology. A conventional PNN consists of Polynomial Neurons, or nodes, located in several layers through a network growth process. In order to generate structurally optimized PNNs, a GA-based design procedure for each layer of the PNN leads to the selection of preferred nodes(PNs) with optimal parameters available within the PNN. To evaluate the performance of the GA-based PNN, experiments are done on a model by applying Medical Imaging System(MIS) data to a multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is modeled with higher accuracy and more superb predictive capability than previously presented intelligent models.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.337-339
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• 2004

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs). Proceeding the layer, this model creates the optimal network architecture through the selection and the elimination of nodes by itself. So, there is characteristic of flexibility. We use a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. GAs is applied to improve the performance with optimal input variables and number of input variables and order. To evaluate the performance of the GAs-based FPNNs, the models are experimented with the use of Medical Imaging System(MIS) data.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.254-259
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• 1998

A new design methology is proposed to identify the structure and parameters of fuzzy model using PNN and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and cubic besides the biquadratic polynomial used in the GMDH. The FPNN(Fuzzy Polynomial Neural Networks) algorithm uses PNN(Polynomial Neural networks) structure and a fuzzy inference method. In the fuzzy inference method, the simplified and regression polynomial inference methods are used. Here a regression polynomial inference is based on consequence of fuzzy rules with a polynomial equations such as linear, quadratic and cubic equation. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture. In this paper, we will consider a model that combines the advantage of both FPNN and PNN. Also we use the training and testing data set to obtain a balance between the approximation and generalization of process model. Several numerical examples are used to evaluate the performance of the our proposed model.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2856-2858
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• 1999

In this paper, the FPNN(Fuzzy Polynomial Neural Networks) algorithm with multi-layer fuzzy inference structure is proposed for the model identification of a complex nonlinear system. The FPNN structure is generated from the mutual combination of PNN (Polynomial Neural Network) structure and fuzzy inference method. The PNN extended from the GMDH(Group Method of Data Handling) uses several types of polynomials such as linear, quadratic and modifled quadratic besides the biquadratic polynomial used in the GMDH. In the fuzzy inference method, simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used Each node of the FPNN is defined as a fuzzy rule and its structure is a kind of fuzzy-neural networks. Gas furnace data used to evaluate the performance of our proposed model.

• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.319-328
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• 2017

The efficiency of port operations is an important indicator of port development. Moreover, there is excess handling capacity in Chinese ports, which results in a slower speed of development. Under the detrimental environment of the international shipping market, it is necessary to improve the operation efficiency of ports for long-term development. This paper provides an assessment of the competitiveness of Chinese seaport companies using the Boston Consulting Group's matrix, and efficiency measurements using a data envelopment analysis and the Malmquist method. This analysis showed that highly efficient companies reformed their development strategies, which should be a solution considered by less efficient companies, such as Shenzhen Yan Tian Port Holdings Co., Ltd.. Although, having high throughput should be reformed in the investment structure. This research will assist port companies in gaining effective operating experience, and governments in establishing strategic planning to enhance the efficiency of port development.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.