• 대한전기학회논문지:시스템및제어부문D
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• 제49권7호
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• 대한전기학회논문지
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• 제39권7호
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• pp.720-728
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• 1990

A learning control method is proposed in this paper, using a knowledge base which contains control rules, data, and patterns of the past experience of a plant. The knowledge for plant control is retrieved from measurement data during operation and continually modified after control performance evaluation. A control method is proposed using tinually modified after control performance evaluation. A control method is proposed using fuzzy model of the plant and a recursive statistic decision method of fuzzy subset for control rule generation. Also, the resulting knowledge-based control algorithm has been applied to aprocess and its performance improvement and proper generation of appropriate control rules have been verified.

• 전기학회논문지
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• 제57권3호
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• pp.466-472
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• 2008

The paper concerns Fuzzy C-Means clustering based fuzzy neural networks (FCM-FNN) and the optimization of the network is carried out by means of hierarchal fair competition-based parallel genetic algorithm (HFCPGA). FCM-FNN is the extended architecture of Radial Basis Function Neural Network (RBFNN). FCM algorithm is used to determine centers and widths of RBFs. In the proposed network, the membership functions of the premise part of fuzzy rules do not assume any explicit functional forms such as Gaussian, ellipsoidal, triangular, etc., so its resulting fitness values directly rely on the computation of the relevant distance between data points by means of FCM. Also, as the consequent part of fuzzy rules extracted by the FCM-FNN model, the order of four types of polynomials can be considered such as constant, linear, quadratic and modified quadratic. Since the performance of FCM-FNN is affected by some parameters of FCM-FNN such as a specific subset of input variables, fuzzification coefficient of FCM, the number of rules and the order of polynomials of consequent part of fuzzy rule, we need the structural as well as parametric optimization of the network. In this study, the HFCPGA which is a kind of multipopulation-based parallel genetic algorithms(PGA) is exploited to carry out the structural optimization of FCM-FNN. Moreover the HFCPGA is taken into consideration to avoid a premature convergence related to the optimization problems. The proposed model is demonstrated with the use of two representative numerical examples.

• 인터넷정보학회논문지
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• 제12권4호
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• pp.107-117
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• 2011

웹의 등장은 전통적인 정보검색을 비롯하여 지식관리와 일반 상거래 등 사회 전 분야의 급격한 변혁을 초래하였다. 그러나 검색엔진은 일반적으로 관련된 계산함수에 의해 순서화된 URL의 방대한 목록을 제공하지만, 관련 없는 정보의 필터링이나 사용자가 필요로 하는 정보의 검색에 많은 시간이 소요된다. 본 논문에서는 웹상의 효율적인 문서검색을 위해서 영역 코퍼스 정보를 바탕으로 확장된 퍼지 계층화 의사결정법(Extended Fuzzy AHP Method : EFAM)과 유사도 기법(SImilarity Technology : SIT)을 결합하고, 감성기준을 고려한 EFASIT(Extended Fuzzy AHP and SImilarity Technology)모델을 제안한다. 제안한 감성기준을 고려한 EFASIT 모델은 다양한 의사결정자들의 퍼지지식의 통합으로 좀 더 명확한 규칙을 생성할 수 있고 의사결정을 하는데 도움을 준다는 것을 실험을 통하여 확인한다.

• 한국항공우주학회지
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• 제31권6호
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• pp.61-71
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• 2003

터보제트엔진 모델에 대한 제어에 있어서, 비교적 잘 설계된 PI 제어기 성능결과를 바탕으로 Takagi-Sugeno형 뉴로-퍼지 추론계를 통한 플랜트 모델의 제어 시스템을 규명함으로서, PI형 T-S 퍼지규칙들을 퍼지제어기를 설계하였다. 이렇게 설계된 제어기의 성능을 향상시키기 위하여, 각 퍼지규칙들을 퍼지 C-Means Algorithm으로부터 각각의 목적 함수군으로 분류한 후, 각 분류군에 대해 규칙간의 가중치가 각 목적함수의 만족도에 부합되도록 하는 기법을 제시하였고, 이를 잘 설계된 T-S형 퍼지제어기에 적용하여 성능을 향상시킴으로써 그 유용성을 보였다.

• 한국정보과학회논문지:데이타베이스
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• 제27권2호
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• pp.141-150
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• 2000

기존의 퍼지 규칙 기반 전문가 시스템 언어에는 크게 세 가지 단점들이 있다. 첫째, 복합 객체 추론 기능이 없으며, 둘째, 의미적으로 이해하기 쉽고 개념적으로 사용하기 용이한 퍼지 추론을 지원하지 못할 뿐 아니라, 세째, 지식 표현과 추론 방식이 기존의 데이터베이스 모텔과 구문이나 의미에서 현격 한 차이를 보이고 있기 때문에 서로 통합되기 어렵다. 본 논문에서는 이 세가지 단점들을 해결하기 위한 퍼지 객체 추론 모델의 정형화를 보이고, GIS 응용을 예로 들어 제시하는 모델이 데이타베이스내 GIS 복합 객체들을 자연스럽게 모델링하고, 이들 사이의 퍼지 추론을 성공적으로 수행함을 보인다.

• 한국지능시스템학회논문지
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• 제16권1호
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• pp.86-93
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• 2006

생물학적으로 동기가 되는 신경망 모델에 기반한 TAM 네트워크는 특별히 패턴분석에 효과적인 모델이다. TAM 네트워크는 입력층, 카테고리층, 출력층으로 구성되어 있다. 입력 및 출력 데이터에 대한 퍼지룰은 TAM 네트워크에서 얻어진다. 각 층에서 링크와 노드를 감소하기 위한 3가지의 프루닝룰을 사용하는 TAM 네트워크를 퍼지 TAM 네트워크라고 한다. 본 논문에서는 퍼지 TAM 네트워크를 건설협력업체의 핵심역량모델의 패턴분석에 적용하고 그 유용성을 보인다.

• 한국정밀공학회지
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• 제12권4호
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• pp.28-38
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• 1995

The flank wear at the minor cutting edge significantly affects the geometric accuracy and surface roughness in finish machining. A fuzzy estimator based on a fuzzy inference algorithm with a max-min composition rule is introduced to evaluate the minor flank wear length. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration signal. These features, dispersions, are used for constructing linguistic rules, and then the fuzzy inferences are carried out with test data sets collected under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 추계학술대회논문집
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• pp.389-390
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• 2008

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1114-1115
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.