• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.327-330
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• 1997

패턴인식중에서 가장 기본적인 문제인 판별문제를 대상으로 러프집합을 이용한 판별분석을 행하는 신경망의 학습알고리즘을 제안한다. 어떤군에 속할 것인가의 경계영역을 명확히 하는 것을 목적으로 한다. 2군 판별의 문제를 각 데이터가 각 군에 속한 정도를 표현하는 소속함수(membership function)을 이용하며, 경계영역에 대한 문제는 소속함수를 구간치 함수로 확장하여 가능성과 필연성을 동시에 표현할 수 있는 학습 알고리즘을 제안한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.373-378
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• 2002

In fuzzy inferencing, most of conventional fuzzy controllers have problems of speed down in floating point operations of fuzzy membership functions in (0,1) as compared with integer operations. Therefore, in this paper, we propose a high-speed fuzzy controller with only integer operations. In this, for fast fuzzy computations, we use a scan line conversion algorithm to convert lines of each fuzzy linguistic term to the set of the closest integer pixels. We also implement a GUI (Graphic User Interface) application program for the convenient environments to modify and input fuzzy membership functions.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.163-168
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• 2010

A linguistic case-based fuzzy reasoning (LCBFR) based on standardized parametric membership functions (SPMF) is proposed. It provides an efficient mechanism for a fuzzy reasoning within linear time complexity. Thus, it can be used to improve the speed of fuzzy reasoning. In the process of LCBFR, linguistic case indexing and retrieval based on SPMF is suggested. It can be processed relatively fast compared to the previous linguistic approximation methods. From the engineering viewpoint, it may be a valuable advantage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.676-683
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• 2005

In this paper, we proposed a method that optimal values of fuzzy control rule base and quantization of membership function are searched by genetic algorithm. Proposed method searched the optimal values of membership function and control rules using genetic algorithm by off-line. Then fuzzy controller operates using these values by on-line. Proposed fuzzy control system is optimized the control rule base and membership function by genetic algorithm without expert's knowledge. We investigated proposed method through simulation and experiment using DC motor and one link manipulator, and confirmed the following usefulness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.1
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• pp.212-219
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• 2006

The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.78-84
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• 1998

In this paper, a fuzzy traffic controller using genetic algorithms is presented. Conventional fuzzy traffic controllers use membership functions generated by humans. However, this approach does not guarantee the optimal solution to design the fuzzy controller. Genetic algorithm is a good problem solving method requiring domain-specific knowledge that is often heuristic. To find fuzzy membership functions showing good performance, a fitness function must be defined. However it's not easy in traffic control to define such a function as a numeric expression. Thus, we use simulation approach, namely, the fitness value of a solution is determined by use of a performance measure that is obtained by traffic simulator. The proposed method outperforms the conventional fuzzy controllers.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.717-722
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• 2004

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer using neural network with weighted fuzzy membership functions (NNWFM). NNWFM is capable of self-adapting weighted membership functions to enhance accuracy in prediction from the given clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from the enhanced bounded sums of n set of weighted fuzzy membership functions. Two number of prediction rules extracted from NNWFM outperforms to the current published results in number of rules and accuracy with 99.41%.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.33-36
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• 1997

로봇이 지니는 지역적 한계성을 극복하기 위하여 구륜 이동용 로봇에 대한 연구가 전세계적으로 진행되어지고 있으나, 구륜이동로보트는 모델링의 불활실성이나 nonholomic등의 제약조건에 의하여 제어기의 설계시 많은 문제들을 지니게 된다. [1][2]. 이러한 어려움을 해결하기 위해 퍼지 알고리즘을 이용한 제어기 설계가 이루어지고 있으나 제한된 범위게 머무르고 있는 상황이다. 본 연구에서는 유전알고리즘에 근거하여 소속함수 및 규칙부의 자율적 조졸을 수행하는 구륜이동로보트의 퍼지 제어기를 한다. 제시된 알고리즘에서 퍼지 입출력 소속함수의 조절을 각각 독립적으로 이루어지며, 출력 소속함수의 유사지표에 근거하여 규칙부의 조절이 이루어진다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.769-771
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• 2005

데이터 특성이 연속적이고 애매할 때 퍼지규칙으로 분류 규칙을 표현하는 것은 매우 유용하고 효과적이다. 그러나 일반적으로 정확하지 않은 데이터 특성에 대해서 소속함수를 결정한다는 것은 어려운 일이다. 본 논문에서는 진화알고리즘을 이용하여 효과적인 퍼지 분류 규칙을 자동으로 생성하는 방법을 제안한다. 제안한 방법에서 규칙의 정확성과 이해성을 고려하여 최적화된 소속함수를 생성하기 위해 진화알고리즘을 사용한다. 먼저 지도 군집화로 진화를 위한 초기 소속함수를 생성한다. 진화알고리즘은 전역적 최적 해를 찾는데 효과적이다. 그러나 시간에 대한 효율성이 낮다. 특히 모델 최적화 문제에서는 개체 평가 단계에서 많은 시간이 소요된다. 따라서 본 논문에서는 전체 데이터를 여러 개의 부분 데이터들로 나누고 개체들은 전체 데이터 대신 매번 부분 데이터를 임의적으로 선택하여 개체를 평가함으로써 수행 시간을 단축시킬 수 있는 진화 방법을 제안한다. 제안한 퍼지 분류 규칙 생성 방법의 타당성을 검증하기 위한 실험 데이터로 UCI에서 제공하는 데이터들을 사용하였으며, 실험 결과는 기존 방법에 비해 평균적으로 더 효과적임을 확인하였다.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.484-493
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• 2011

The MPPT algorithm using neuro-fuzzy controller is proposed to improve the performance of fuzzy controller in this paper. The width of membership function and fuzzy rule have an effect on the performance of fuzzy controller. The neuro-fuzzy controller has the response characteristic which is superior to the existing fuzzy controller, because of using the optimal width of the fuzzy membership function through the neural learning. The superior control characteristic of a proposed algorithm is confirmed through simulation and experiment results.