• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.384-389
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• 1998

본 논문은 특정 응용에 적합한 퍼지 제어기의 최적 설계 파라메터(퍼지 규칙과 소속 함수)를 찾는데 역전파 학습 과정과 유전 알고리즘을 결합한 Lamarckian 상호적응 기법을 이용한 뉴로-퍼지 제어기의 새로운 설계 방법을 제안한다. 설계 파라메타들은 진화에 의한 전역적 탐색을 통해 높은 포함값과 유용한 퍼지 규칙들을 갖는 규칙 베이스와 작은 근사화 오차와 좋은 제어 성능을 갖는 소속 함수들을 얻도록 제어기간 파라메타 조절을 수행하며, 학습에 의한 국부적 탐색을 통해 각 퍼지 제어기가 원하는 제어 결과를 나타내도록 제어기내 파라메타 조절을 수행한다. 제안한 상호적응 설계 방법은 유전 알고리즘의 모든 세대에서 역전파 학습이 이루어지므로 보다 좋은 근사화 능력을 나타나고, 사용한 무게 중심 비퍼지화기가 정확한 비퍼지화값을 계산하므로 보다 좋은 제어 성능을 가지며, 퍼지 규칙 베이스와 소속 함수들의 최적화 탐색 과정이 입출력 공간의 같은 퍼지 분할 상에서 통합된 적응 함수에 의하여 동시에 수행되므로 탐색을 위한 작업 공간이 아주 작아지는 장점이 있다. 시뮬레이션 결과는 Lamarckian 상호 적응에 의해 얻어진 FLC가 퍼지 규\ulcorner 수, 근사화 능력, 제어 성능등 모든면에서 다른 방법에 의해 얻어진 FLC보다 가장 우수함을 보여준다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1999.11a
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• pp.388-395
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• 1999

본 논문의 목적은 퍼지 엔트로피를 이용하여 비선형신호를 예측하는 것이다. 이 방법은 분할된 여러 부 공간(subspace)에 대해 입력 데이터로부터 퍼지 엔트로피를 이용하여 각각의 규칙에 등급을 정하여 불필요한 제어규칙을 제거하여 바람직한 규칙베이스를 구성하도록 한 것이다. 적용되는 퍼지 신경망의 기본적인 구조는 퍼지 제어기의 규칙베이스와 추론의 과정을 신경회로망을 이용하여 구현하며 퍼지 제어규칙의 매개변수들은 역전파 알고리즘에 의해 적응되어진다. 또한 매개변수의 수를 줄이기 위하여 제어규칙의 결론부의 출력값은 신경망의 가중치로 구성하였다. 결국 퍼지 신경망의 복잡도를 줄일 수 있다. Mackey-Glass 시계열의 예측에 대한 컴퓨터 시뮬레이션을 통하여 본 논문에서 제안한 방법의 효율성을 입증하고, 제안된 방법을 EEG 생리신호 분석에 이용될 수 있다.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1246-1256
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• 1998

MIB(Management Information Base), one of the key components of network management system, is a conceptual repository for the information of the various managed objects. MIB stores and manages all the structural and operational data of each managed resources. Therefore, MIB should be protected properly from inadvertant user access or malicious attacks. International standard ISO/IEC 10164-9 describes several managed object classes for the enforcement of MIB security. Those managed object classes described access control rules for security policy. But the exact authorization procedures using those newly added managed object classes are not presented. In this paper, we divide managed object classes into two groups, explicit and implicit ones, and describe the access authorization procedure in Z specification language. Using Z as a description method for both authorization procedure and GDMO's action part, the behaviour of each managed object class and access authorization procedure is more precisely and formally defined than those of natural language form.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.3
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• pp.241-245
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• 2001

The existing fuzzy traffic controllers construct the rule-base based on the intuitive knowledge and experience or the standard rule-base, but the rule-base constructed by the above methods has difficulty in representing exactly and detailedly the control knowledge of the export and the operator. Therefore, in this paper, we propose a method that can improve the performance of the fuzzy traffic control by designing the fuzzy traffic controller which represents the control knowledge more exactly. The proposed method so modifies the position and shape of the fuzzy membership function based on the input-output data clustering that the fuzzy traffic controller can represent the control knowledge more exactly. Our method use the rough control knowledge based on intuitive knowledge and experience as the evaluation function for clustering the input-output data. The fuzzy traffic controller designed by the our method could represent the control knowledge of the expert and the operator more exactly, and it outperformed the existing controller in terms of the number of passed vehicles and the wasted green-time.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.10
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• pp.1108-1120
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• 1990

The present study is to develop an intelligent control system for flexible manufacturing system, which is suitable for a variety of manufacturing types with smaller production rates. The controller is designed to integrate heuristic rules with optimization techniques for loading as well as flow rate of parts and ultimately meeting performance indices. The control function implemented by an optimization technique is to calculate short term production rates of parts. The heuristic control determined by production rules requires knowledge base to evaluate selected loading alternatives according to short term production rate and current process information, and also to determine final decision pertaining to loading. In this case, the knowledge base is constructed using the rules for evaluating alternatives, decision criteria, and flow control of parts in manufacturing system. The database is formulated by means of managing and updating current process information. A graphic system to monitor current status of the function and operation of manufacturing system is developed, and computer simulation is carried out to evaluate the performance of the proposed controller.

• Journal of Korea Multimedia Society
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• v.3 no.2
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• pp.201-208
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• 2000

In this paper We describe an algorithm which is devised for 4he partition o# the input space and the generation of fuzzy rules by the fuzzy entropy and tested with the time series prediction problem using Mackey-Glass chaotic time series. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rules base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. The Proposed algorithm has been naturally derived by means of the synergistic combination of the approximative approach and the descriptive approach. Each output of the rule's consequences has expressed with its connection weights in order to minimize the system parameters and reduce its complexities.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.73-83
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• 1995

In this paper, we propose a generating method for the optimal rules of the fuzzy rule base using evolution algorithms. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and knowledge. can be intelligent control. The a, pp.oach presented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which is the defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method in non-linear systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.6
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• pp.591-596
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• 2000

본 논문에서는, 퍼지 신경망 시스템에 대한 최적의 규칙 베이스의 생성과 초기화를 이루기 위하여 웨이브릿 이론을 기반으로 한 퍼지 신경망 구조를 제안한다. 제안한 웨이브릿 기반의 퍼지 신경망 구조(WFNN)에서는 퍼지-신경망에 대하여 웨이브렛 함수의 성질과 다운스트레칭 메카니즘에 의하여 초기의 최적 퍼지 규칙 베이스를 구성하고 은닉층의 노드 개수를 최적화시키며, 에러 역전파 알고리즘에 의하여 각 파라미터의 조절과 학습이 진행된다. 역진자 시스템에 대한 모의 실험을 통하여 제안한 웨이브릿 기반의 퍼지 신경망 제어 시스템의 우수성을 검증하였다.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.97-104
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• 2002

In this paper we describe the method which optimizes the partition of the input space by means of measure of fuzziness for fuzzy neural network. It covers its generation of fuzzy rules for input sub space. It verifies the performance of the system depended on the various time interval of the input. This method divides the input space into several fuzzy regions and assigns a degree of each of the generated rules for the partitioned subspaces from the given data using the Shannon function and fuzzy entropy function generating the optimal knowledge base without the irrelevant rules. In this scheme the basic idea of the fuzzy neural network is to realize the fuzzy rule base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by the steepest descent algorithm. According to the input interval the proposed inference procedure proves that the fast convergence of root mean square error (RMSE) owes to the optimal partition of the input space