• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.292-295
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• 2003

로봇의 기능이 다양해지며 복잡해지고 있다 주위의 환경을 감지하는 센서로는 거리정보 뿐만이 아니라 영상 정보, 음성 정보까지 이용하고 있다 본 논문에서는 다양한 입력정보를 가지고 로봇을 제어하기 위한 알고리즘으로 2-Layer Fuzzy Control을 제안한다 장애물 회피의 경우에 다수의 초음파 센서를 이용하였는데 이것을 앞쪽, 왼쪽, 오른쪽으로 분류하여 3개의 sub-controller를 가지고 퍼지 추론을 한 다음 2단계에서는 이 3개의 sub-controller의 출력으로 조합된 퍼지 추론을 하여 통합적인 추론을 한다 본 논문에서는 2-Layer Fuzzy Controller와 비슷한 구조를 갖는 Hierarchical Fuzzy Controller와 성능비교를 하였으며 Robot following에도 적용하여 각각에 대한 시뮬레이션과 실험을 통해 확인한다.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.50-56
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• 2001

As the fuzzy control is applied to servo system, the hardware implementation of the fuzzy information systems requires the high speed operations, short real time control and the small size systems. The aims of this study is to develop hardware of the fuzzy information systems to be apply to servo system. In this paper, we propose a calculation method of approximate reasoning for fuzzy control based on $\alpha$ -level set decomposition of fuzzy sets by quantize $\alpha$ -cuts. This method can be easily implemented with analog hardware. The influence of quantization Bevels of $\alpha$-cuts on output from fuzzy inference engine is investigated. It is concluded that 4 quantization levels give sufficient result for fuzzy control performance of dc servo system. The hardware implementation of proposed operation method and of the defuzzification by gravity center method which is directly converted to PWM actuating signal is also presented. It is verified useful with experiment for dc servo system.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.81-89
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• 1996

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 eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.5
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• pp.555-565
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• 1999

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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.138-144
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• 1994

An inference network is proposed as a tool for bidirectional approximate reasoning. The inference network can be designed directly from the given fuzzy data(knowledge). If a fuzzy input is given for the inference netwok, then the network renders a reasonable fuzzy output after performing approximate reasoning based on an equality measure. Conversely, due to the bidirectional structure, the network can yield its corresponding reasonable fuzzy input for a given fuzzy output. This property makes it possible to perform forward and backward reasoning in the knowledge base system.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.219-225
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• 2006

This paper presents a method of object classification based on discrete wavelet transform (DWT) and fuzzy inference(FI). It concentrated not only on the design of fuzzy inference algorithm which is suitable for low speed uninhabited transportation such as, conveyor but also on the minimize the number of fuzzy rule. In the preprocess of feature extracting, feature parameters are extracted by using characteristics of the coefficients matrix of DWT. Such feature parameters as area, perimeter and a/p ratio are used obtained from DWT coefficients blocks. Secondly, fuzzy if - then rules that can be able to adapt the variety of surroundings are developed. In order to verify the performance of proposed scheme, In the middle of fuzzy inference, the Mamdani's and the Larsen 's implication operators are utilized. Experimental results showed that proposed scheme can be applied to the variety of surroundings.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.11a
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• pp.451-456
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• 2007

유비쿼터스 컴퓨팅 환경을 구축하기 위해서는 사용자 및 주변 상황에 관한 인지기술이 필수적이다. 이에 따라 이기종 분산형 시스템에서 언어와 기종에 영향을 받지 않고 사용자 Context를 인지하고 표현하는 문제는 해결해야할 중요한 과제로 대두되었다. 이에 따라, 본 논문에서는 이 과제를 해결하기 위하여 시맨틱 웹 기술 및 퍼지 개념을 이용하여 사용자 Context를 기술하는 것을 제안한다. 온톨로지는 컴퓨터가 정보자원의 의미를 파악하고 자동적으로 처리할 수 있도록 고안된 지식표현 언어이므로 이기종 시스템 하에서의 사용자 Context를 표현하는데 적합하다. 한편, 사용자가 접할 실세계의 환경은 일반집합(Crisp Set)으로 표현하기 힘들기 때문에 본 논문에서는 퍼지개념과 표준 웹 온톨로지 언어 OWL이 융합된 Fuzzy OWL언어를 사용했다. 본 논문에서 제안하는 방법은 Context를 Fuzzy OWL로 표현하기 위하여 먼저 사용자가 접한 환경정보들을 수치로 표현한다. 그리고 이를 OWL로 기술하며 OWL로 표현된 사용자 Context를 Fuzzy OWL로 변환한다. 마지막으로 퍼지 개념이 포함된 사용자 Context를 이용하여 자동적인 상황인지가 가능한지 여부를 퍼지 추론 엔진인 FiRE를 사용하여 실험한다. 본 논문에서 제시한 방법을 사용하면 이기종 분산시스템에서도 사용할 수 있는 형태로 Context를 기술할 수 있다. 그리고 기술된 Context를 기반으로 현재 사용자가 접한 환경의 상태를 추론할 수 있다. 또한 퍼지 기술 로직 언어(Fuzzy Description Logic)기반 추론기인 FiRE를 이용하여 이를 검증한다.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.545-557
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• 2001

In this paper we propose a hierarchical classification algorithm based on rough membership function which can reason a new object approximately. We use the fuzzy reasoning method that substitutes fuzzy membership value for linguistic uncertainty and reason approximately based on the composition of membership values of conditional sttributes Here we use the rough membership function instead of the fuzzy membership function It can reduce the process that the fuzzy algorithm using fuzzy membership function produces fuzzy rules In addition, we transform the information system to the understandable minimal decision information system In order to do we, study the discretization of continuous valued attributes and propose the discretization algorithm based on the rough membership function and the entropy of the information theory The test shows a good partition that produce the smaller decision system We experimented the IRIS data etc. using our proposed algorithm The experimental results with IRIS data shows 96%~98% rate of classification.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.103-108
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• 2001

본 논문은 대량의 퍼지 데이터를 고속으로 전송 및 추론하기 위한 PCI 기반 병렬 퍼지 시스템을 구현한다. 많은 퍼지 데이터의 고속전송을 위해 PCI 인터페이스를 사용하고, 병렬 퍼지 추론 시스템을 위한 병렬 퍼지 모듈들을 FPGA로 설계하여 PCI 타겟 코어로서 병렬로 동작하게 한다. 이러한 시스템을 VHDL을 사용하여 설계 및 구현하였다. 본 시스템은 고속의 퍼지추론을 요하는 시스템 또는 대규모의 퍼지 전문가 시스템 등에 활용될 수 있다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1050-1057
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• 2004

The purpose of study is development of a fuzzy controller which independent of a computer and its software for fuzzy control of servo system. This paper describes a method of approximate reasoning for fuzzy control of servo system, based on decomposition of $\alpha$-level fuzzy sets, It is propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion in cases where the output variable u directly is generated PWM. The effectiveness of quantified $\alpha$-levels on input/output characteristics of fuzzy controller and output response of DC servo system is investigated. It is concluded that $\alpha$-cut 4 levels give a sufficient result for fuzzy control performance of DC servo system. The experimental results shows that the proposed hardware method is effective for practical applications of DC servo system.