공정제어 시스템의 지능형 모델링을 위한 퍼지 뉴럴네트워크 구조의 설계

吳聖權,黃炯秀,安泰天 원광대학교 공업기술개발연구소 1994 工業技術開發硏究誌 Vol.14 No.-

In this paper, new design methodology of rule-based fuzzy modeling is Presented in which Process simulation based on the digital process control system is implemented with fuzzy-neural networks(FNNs). The proposed rule-based fuzzy modeling implements system structure and Parameter identification in the efncient form of "If… THEN…" using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules , and neural networks(NNs). The structure identification of fuzzy implication rules is carried out utilizing fuzzy c-means clustering and the proposed FNNs have the neural network structures with the connection weights of particular meanings for acquiring fuzzy inference rules and for tuning membership functions. The optimal fuzzy rules are extracted by tuning learning rates and momentum coefficients using the modified complex method. The feasibility of the Proposed method is examined through simulation, which reveals that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

퍼지추론 방법에 의한 퍼지동정과 하수처리공정시스템 응용

오성권,주영훈,남위석,우광방 대한전자공학회 1994 전자공학회논문지-B Vol.b31 No.6

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

퍼지추론 방법을 이용한 교통경로선택의 모델링

오성권 圓光大學校 1996 論文集 Vol.31 No.2

퍼지모델링의 설계 방법을 교통경로선택의 모델동정을 위하여 제안한다. 제안된 퍼지모델은 최적화이론, 퍼지구현규칙을 사용하여 "IF... THEN..."의 효율적인 형태로 시스템구조와 파라미터 동정을 시행한다. 이 논문에서 간략추론, 선형추론, 변형된 선형추론의 3가지종류의 퍼지모델링 방법을 제시한다. 이 퍼지추론 방법은 인간의 교통행동의 정확한 추정과 정밀한 묘사를 위해 교통경로선택 모델을 개발하기 위해 이용된다. 퍼지규칙의 전반부 구조와 파라미터를 동정하기 위해 개선된 컴플렉스법을 사용하고, 최적 후반부 파라미터를 동정하기 위해 최소자승법이 사용된다. 교통경로선택 데이타가 제안된 퍼지모델 성능을 평가하기 위해 사용된다. 제안된 방법이 기존의 다른 연구들-즉 BL, PS, FL, NN, FNNs모델 등- 보다 더 높은 정확도를 가진 퍼지모델을 생성함을 보인다. A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems. The proposed fuzzy modeling implements system structure and parameter identification in the efficient form of "IF..., THEN..", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type 1), linear inference (type 2), and proposed modified- linear inference (type 3). The fuzzy inference method are utilized to develop the route choice model in terms of accurate estimation and precise description of human travel behavior. In order to identify premise structure and parameter of fuzzy implication rules, improved complex method is used and the least square method is utilized for the identification of optimum consequence parameters. Data for route choice of traffic problems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than previous other studies-BL(binary logic) model, PS(production system) model, FL(fuzzy logic) model, NN(neural network) model, and FNNs (fuzzy-neural networks) model-.

데이터 정보입자 기반 퍼지 추론 시스템의 최적화

오성권,박건준,이동윤 대한전기학회 2004 전기학회논문지 D Vol.53 No.6(D)

In this paper, we introduce and investigate a new category of rule-based fuzzy inference system based on Information Granulation(IG). The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of "If..., then..." statements, and exploits the theory of system optimization and fuzzy implication rules. The form of the fuzzy rules comes with three types of fuzzy inferences: a simplified one that involves conclusions that are fixed numeric values, a linear one where the conclusion part is viewed as a linear function of inputs, and a regression polynomial one as the extended type of the linear one. By the nature of the rule-based fuzzy systems, these fuzzy models are geared toward capturing relationships between information granules. The form of the information granules themselves becomes an important design features of the fuzzy model. Information granulation with the aid of HCM(Hard C-Means) clustering algorithm helps determine the initial parameters of rule-based fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial function being used in the premise and consequence part of the fuzzy rules. And then the initial parameters are tuned (adjusted) effectively with the aid of the improved complex method(ICM) and the standard least square method(LSM). In the sequel, the ICM and LSM lead to fine-tuning of the parameters of premise membership functions and consequent polynomial functions in the rules of fuzzy model. An aggregate objective function with a weighting factor is proposed in order to achieve a balance between performance of the fuzzy model. Numerical examples are included to evaluate the performance of the proposed model. They are also contrasted with the performance of the fuzzy models existing in the literature.

Fuzzy Identification by Means of an Auto-Tuning Algorithm and a Weighted Performance Index

오성권,Oh, Sung-Kwun Korean Institute of Intelligent Systems 1998 한국지능시스템학회논문지 Vol.8 No.6

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

하이브리드 동정 알고리즘에 의한 최적 퍼지 시스템에 관한 연구

오성권 한국지능시스템학회 1999 한국지능시스템학회논문지 Vol.9 No.5

복잡하고 비선형적인 시스템의 규칙베이스 퍼지모델링을 위하여 퍼지시스템의 최적 동정알고리즘을 연구한다. 비선형 시스템은 퍼지모델의 입력변수와 퍼지 입력공간 분할에 의한 구조동정과 파라미터 동정을 통해 표현된다. 본 논문에서 규칙베이스 퍼지모델링은 비선형 시스템을 위해 퍼지추론방법과 두 종류의 최적화 이론의 결합에 의한 하이브리드 구졸를 이용하여 시스템 구조와 파라미터동정을 수행한다. 퍼지모델의 추론방법은 간략추론 및 선형추론에 의한다. 제안된 하이브리드 최적 동정 알고리즘은 유전자 알고리즘과 개선된 콤플렉스 방법을 이용한다. 여기서 유전자 알고리즘은 전반부 퍼지규칙의 멤버쉽함수의 초기 파라미터들을 결정하기 위해 사용되고 강력한 자동동조 알고리즘인 개선된 콤플렉스 방법은 정교한 파라미터들을 얻기 위해 수행된다. 따라서 최적 퍼지모델을 위해 전반부 파라미터 동정에는 하이브리드형의 최적 알고리즘을 이용하고 후반부 동정에는 최소자승법을 이용한다. 또한 학습과 테스트 데이터에 의해 생성된 퍼지모델의 성능결과 사이의 상호균형을 얻기 위해 하중계수를 가지는 합성 성능지수를 제안한다. 제안된 모델의 성능평가를 위해 두가지 수치적 예를이용한다. The optimal identification algorithm of fuzzy systems is presented for rule-based fuzzy modeling of nonlinear complex systems. Nonlinear systems are expressed using the identification of structure such as input variables and fuzzy input subspaces, and parameters of a fuzzy model. In this paper, the rule-based fuzzy modeling implements system structure and parameter identification using the fuzzy inference methods and hybrid structure combined with two types of optimization theories for nonlinear systems. Two types of inference methods of a fuzzy model are the simplified inference and linear inference. The proposed hybrid optimal identification algorithm is carried out using both a genetic algorithm and the improved complex method. Here, a genetic algorithm is utilized for determining initial parameters of membership function of premise fuzzy rules, and the improved complex method which is a powerful auto-tuning algorithm is carried out to obtain fine parameters of membership function. Accordingly, in order to optimize fuzzy model, we use the optimal algorithm with a hybrid type for the identification of premise parameters and standard least square method for the identification of consequence parameters of a fuzzy model. Also, an aggregate performance index with weighting factor is proposed to achieve a balance between performance results of fuzzy model produced for the training and testing data. Two numerical examples are used to evaluate the performance of the proposed model.

퍼지-뉴럴 모델링과 비선형 공정 모델의 최적화

오성권,노석범,황형수,박희순 제어로봇시스템학회 1995 제어로봇시스템학회 국내학술대회 논문집 Vol.1 No.2

GMDH 방법에 의한 FPNN 일고리즘과 폐스처리공정에의 응용

오성권,황형수,안태천,Oh, Sung-Kwon,Hwang, Hyung-Soo,Ahn, Tae-Chon 한국지능시스템학회 1997 한국지능시스템학회논문지 Vol.7 No.2

본 논문에서는 복잡한 비선형 시스템의 모델동정을 위해 퍼지모델링의 새로운 방법이 제안된다. 제안된 FPNN모델링은 공정시스템의 입출력 데이터로부터 GMDH방법과 퍼지구현규칙을 이용하여 시스템의 구조와 파라미터 동정을 구현한다. 퍼지구현규칙의 전반부 구조와 파라미터 동정을 위하여 GMDH 방법과 희귀다항식 퍼지추론 방법이 사용되고 최적 후반부 파라미터 동정을 위하여 최소자승법이 사용된다. 가스로 시계열데이타 및 하수처리시스템의 활성화의 공정 데이터가 제안한 FPNN 모델링의 성능을 평가하기 위해 상용된다. 제안된 방법이 기존의 다른 논문과 비교하여 더 높은 정확도를 가진 지능형 모델을 생성함을 보인다. In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

활성오니공정의 모델링 및 제어기의 설계

오성권 公州大學校工科大學生産技術硏究所 1993 論文集 Vol.1 No.-

본 논문에서는 활성오니공정(A.S.P)의 퍼지모델링과 모델을 기반으로 한 퍼지논리제어기를 제안하였다. 구조동정에서는 Fuzzy-C-mean(FCM) 클러스팅잉 알고리즘을 사용하였고, 퍼지 파라메타를 동정하기위해 콤플렉스 기법과 최소자승법을 이용하였다. 모델 기반 퍼지제어기는 동정화된 활성오니공정에서 생성된 규칙에 의해 설계되었다. 제안된 방법의 가능성은 활성오니공정의 입출력관계를 결정하기위한 퍼지모델의 동정을 통해 평가된다. In this study, we proposed the fuzzy modeling method and designed a model-based fuzzy logic controller for Activated Sludge Process(A.S.P) in sewage treatement. The identification of the structure of fuzzy implications is carried out by use of fuzzy c-means clustering algorithm. And to identify the parameters of fuzzy implications, we used the complex and the least square method. For the efficient parameter identification the complex method is implemented. The model-based fuzzy controller is designed by rules generated from the identified A.S.P fuzzy model. The feasibility of the proposed approach is evaluated through the idetification of the fuzzy model to describe an input-output felation of the A.S.P.A. The performance of identified model-based model-based fuzzy controller is evaluated through the computer simulations.

퍼지 GMDH 모델링과 비선형공정에의 응용

오성권,노석범,남궁문,안태천 제어로봇시스템학회 1995 제어로봇시스템학회 국내학술대회 논문집 Vol.1 No.2