• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.157-157
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• 2000

In the fuzzy control for the multi-variable system, it is difficult to obtain the fuzzy rule. Therefore, the parallel structure of the independent single input-single output fuzzy controller using a pairing between the input and output variable is applied to the multi-variable system. The concept of relative gain matrix is used to obtain the input-output pairs. However, among the input/output variables which are not paired the interactive effects should be taken into account. these mutual coupling of variables affect the control performance. Therefore, for the control system with a strong coupling property, the control performance is sometimes lowered. In this paper, the effect of mutual coupling of variables is considered by tile introduction of a simple compensator. This compensator adjusts the degree of coupling between variables using a neural network. In this proposed neuro-fuzzy controller, the Neural network which is realized by back-propagation algorithm, adjusts the mutual coupling weight between variables.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.824-826
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• 1999

In this paper, we design an Fuzzy-Neural Networks(FNN) by means of divisions of fuzzy input space with multi-input variables. Fuzzy input space of Yamakawa's FNN is divided by each separated input variable, but that of the proposed FNN is divided by mutually combined input variables. The membership functions of the proposed FNN use both triangular and gaussian membership types. The parameters such as apexes of membership functions, learning rates, momentum coefficients, weighting value, and slope are adjusted using genetic algorithms. Also, an aggregate objective function(performance index) with weighting value is utilized to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model, we use the data of sewage treatment process.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.239-246
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• 2003

Statisticians, or data miners, are often requested to assess the importances of input variables in the given supervised learning model. For the purpose, one may rely on separate ad hoc measures depending on modeling types, such as linear regressions, the neural networks or trees. Consequently, the conceptual consistency in input variable importance measures is lacking, so that the measures cannot be directly used in comparing different types of models, which is often done in data mining processes, In this short communication, we propose a unified approach to the importance measurement of input variables. Our method uses sensitivity analysis which begins by perturbing the values of input variables and monitors the output change. Research scope is limited to the models for continuous output, although it is not difficult to extend the method to supervised learning models for categorical outcomes.

• 대한산업공학회지
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• 제40권1호
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• pp.84-99
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• 2014

In this study, determinant input-output variables are identified for calculating Data Envelopment Analysis (DEA) efficiency scores relating to evaluating the efficiency of government-sponsored research and development (R&D) projects. In particular, this study proposes a systematic framework of design and analysis of experiments, called "all possible DEAs", for pinpointing DEA determinant input-output variables. In addition to correlation analyses, two modified measures of time series analysis are developed in order to check the similarities between a DEA complete data structure (CDS) versus the rest of incomplete data structures (IDSs). In this empirical analysis, a few DEA determinant input-output variables are found to be associated with a typical public R&D performance evaluation logic model, especially oriented to a mid- and long-term performance perspective. Among four variables, only two determinants are identified : "R&D manpower" ($x_2$) and "Sales revenue" ($y_1$). However, it should be pointed out that the input variable "R&D funds" ($x_1$) is insignificant for calculating DEA efficiency score even if it is a critical input for measuring efficiency of a government-sonsored R&D project from a practical point of view a priori. In this context, if practitioners' top priority is to see the efficiency between "R&D funds" ($x_1$) and "Sales revenue" ($y_1$), the DEA efficiency score cannot properly meet their expectations. Therefore, meticulous attention is required when using the DEA application for public R&D performance evaluation, considering that discrepancies can occur between practitioners' expectations and DEA efficiency scores.

• 조명전기설비학회논문지
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• 제15권1호
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• pp.70-77
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• 2001

본 논문에서는 고도정수처리설비에 활용되는 오존접촉조의 반응 특성을 퍼지 모델 형태로 표현하는 방법을 제안한다. 퍼지모델에 사용될 입력 및 출력 변수들은 오존처리의 목적과 정수장의 수질관리항목을 기준으로 선정하였다. 제안된 입력 변수들은 용존유기탄소농도, $UV_{254}$흡광도, 과망간산칼륨소비량, 주입오존농도, 수온 및 접촉시간이며, 출력변수들은 용존유기탄소농도, $UV_{254}$흡광도 및 과망간산칼륨소비량이다. 입력변수들에 대한 소속도 함수들은 삼각형 형태로 설계하였으며, 파이롯플랜트에서 취득한 조업데이터를 참고하여 소속도를 결정했다. 퍼지모델의 결론부는 선형식 형태로 설계하였으며, 선형식에 포함되는 상수들은 조업데이터를 이용하여 최소자승법으로 구했다. 또한 출력 변수들간에 상호 영향이 없다는 특성으로부터 전체 퍼지모델로을 각 변수별로 독립적인 기능을 갖는 3개의 부분 퍼지모델로 분할하여 설계함으로서 계산과 이해의 편리를 도모하였다. 모의실험을 통해 제안된 퍼지모델의 타당성을 확인해 본 결과, 모델의 튜닝시에 사용한 입력 데이터에 대해 퍼지모델의 출력이 조업데이터와 거의 동일함을 알 수 있었다.

• 산업공학
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• 제12권4호
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• pp.551-556
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• 1999

In this paper, we compare the performance of three data mining classification algorithms(neural network, decision tree, logistic regression) in consideration of various characteristics of categorical input and output data. $2^{4-1}$. 3 fractional factorial design is used to simulate the comparison situation where factors used are (1) the categorical ratio of input variables, (2) the complexity of functional relationship between the output and input variables, (3) the size of randomness in the relationship, (4) the categorical ratio of an output variable, and (5) the classification algorithm. Experimental study results indicate the following: decision tree performs better than the others when the relationship between output and input variables is simple while logistic regression is better when the other way is around; and neural network appears a better choice than the others when the randomness in the relationship is relatively large. We also use Taguchi design to improve the practicality of our study results by letting the relationship between the output and input variables as a noise factor. As a result, the classification accuracy of neural network and decision tree turns out to be higher than that of logistic regression, when the categorical proportion of the output variable is even.

• 디지털융복합연구
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• 제12권4호
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• pp.277-283
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• 2014

퍼지모델링은 일반적으로 주어진 데이터를 이용하고 퍼지규칙은 입력변수를 선정하고 각 입력변수에 대한 입력공간을 분할함으로써 입력변수 및 공간분할에 의해 확립된다. 퍼지규칙의 전반부는 입력변수, 공간분할 수 및 소속 함수를 선정하고 본 논문에서 후반부는 선형추론 및 변형된 이차식에 의해 다항식함수의 형태로 나타낸다. 전반부 파라미터의 동정은 입출력 데이터의 최소값과 최대값을 이용하는 최소-최대 방법 및 입출력 데이터를 군집으로 형성하는 C-Means 클러스터링 알고리즘을 사용하여 입력공간을 분할한다. 각 규칙의 후반부 파라미터들, 즉 다항식의 계수들의 동정은 표준최소자승법에 의해 수행된다. 본 논문에서 전반부 소속 함수는 사다리꼴형 멤버쉽 함수를 사용하여 입력공간을 분할하고 비선형공정에서 널리 이용되는 가스로데이터를 사용하여 성능을 평가한다.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.487-493
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• 2004

This paper consider a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the four vital few input variables are jointly obtained by maximizing TIP life time using DOE.

• 품질경영학회지
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• 제33권3호
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• pp.135-148
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• 2005

This paper considers a six sigma project for reducing the project costs through project risk management. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A risk management process map is used to identify process input and output variables. Seven key process input variables are selected by using C&E diagram and X-Y matrix and finally four vital few input variables are selected by the related statistical analysis. The optimum alternatives of the vital few input variables are obtained by the method of PUGH matrix. The process is running on control plan and we obtained substantial project cost reductions in early stage of the control phase.

• 품질경영학회지
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• 제33권1호
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• pp.88-98
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• 2005

This paper considers a six sigma project for improving the TIP life time in a spot welding process. The project follows a disciplined process of five phases: define, measure, analyze, improve, and control. A process map is used to identify process input and output variables. Nine key process input variables are selected by using C&E matrix and FMEA, and finally four vital few input variables are selected from analyze phase. The optimum process conditions of the vital few input variables are jointly obtained by maximizing TIP life time using DOE and alternative selection method.