• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.925-928
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• 1993

In this paper the caracteristics of the fuzzy flip-flop which was proposed as a fuzzy sequential circuit is firstly mentioned. Secondly the circuit construction of typical fuzzy flip-flip circuits using VHDL (Very high speed integrated circuit Hardware Description Language) compiler and simulator is presented. Finally the possibility of the application of the fuzzy sequential circuit will be mentioned.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.20-25
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• 2002

This paper presents a genetic programming based evolutionary strategy for on-line adaptive learnable evolvable hardware. Genetic programming can be useful control method for evolvable hardware for its unique tree structured chromosome. However it is difficult to represent tree structured chromosome on hardware, and it is difficult to use crossover operator on hardware. Therefore, genetic programming is not so popular as genetic algorithms in evolvable hardware community in spite of its possible strength. We propose a chromosome representation methods and a hardware implementation method that can be helpful to this situation. Our method uses context switchable identical block structure to implement genetic tree on evolvable hardware. We composed an evolutionary strategy for evolvable hardware by combining proposed method with other's striking research results. Proposed method is applied to the autonomous mobile robots cooperation problem to verify its usefulness.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1729-1734
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• 2005

The use of gantry crane systems for transporting payload is very common in industrial application. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. To overcome this problem, this paper describes development of an intelligent gantry crane system based on the mechatronic design. A lab-scale gantry crane is designed and then its intelligent controllers are developed. Fuzzy logic controllers are adopted, designed and implemented for controlling payload position as well as the swing angle of the gantry crane. The performance of the intelligent gantry crane system is evaluated on a hardware-in-the-loop simulation (HILS) environment. Moreover robustness of the proposed system is also evaluated. The result shows that the intelligent gantry crane system designed based on the mechatronic design approach has better performance compared with the automatic gantry crane system controlled by classical PID controllers. Moreover simulation result shows that the intelligent gantry crane system is more robust to parameter variation than the automatic gantry crane system.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.989-992
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• 1993

In this paper, we describe hardware architecture of fuzzy processors for reasoning involving fuzzy control“Heuristics”. This we believe will lead to fuzzy systems that are closer to the way humans process domain knowledge for decision making. One noticeable beneficial effect based on our notion of fuzzy heuristics is the significantly reduced number of rules required.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.838-841
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• 1993

This paper describes a fuzzy network circuit of analogue and digital mixed operation. The circuits are suggested for membership function, MIN function and normalization function using either linear voltage-controlled MOSFET resistance or pulse stream operation. The analogue-digital hybrid fuzzy hardware is extensible to the fuzzy-neural network as its basic configurations are already used in URAN-I of 135,424 synaptic connections.

• 한국지능시스템학회논문지
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• 제10권6호
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• pp.575-583
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• 2000

본 논문에서 임의의 데이터가 입력되면 기준 영상 중에서 가장 유사도가 큰 영상을 찾아 국부 승리자로 선택하고, 그 국부 승리자 중에서 전체 승리자를 선택하여 최종 출력값을 얻는 계층적 FGNN(Fuzzy Genetic Neural Network)을 제안하고, 이에 하이브리드 퍼지 소속함수와 유전자 알고리즘을 적용하였다. 하이브리드 퍼지 소속함수는 입력 값을 0~1 사이의 값으로 함으로써 시스템의 속도를 빠르게 하고 유전자 알고리즘을 입력값을 일정한 오차 이내로 하여 최적의 영상을 얻도록 하였다. 위의 계층적 FGNN 알고리즘을 회로 설계 및 검증하였다. 또한 제안한 FGNN을 이용하여 영상에 포함된 잡음을 제거하고, 이와 유사한 구조를 가진 FDNN(Fuzzy Decision Neural Network) 성능보다 FGNN의 성능이 우수함을 여러 가지 영상을 통하여 확인하였다. 또한 모의 실험 결과 영상에 대한 평균자승오차(MSE : Mean Square Error)를 비교하였으며, 그 결과 하이브리드 퍼지 함수와 유전자 알고리즘을 적용한 FGNN이 메디안 필터, OC, CO, FDNN 등에 비해 우수함을 확인하였다. FGNN 알고리즘을 Top-Down 방식으로 VHDL(VHSIC Hardware description Language)을 이용하여 코딩(Coding)하고, Synopsys 툴을 이용하여 하드웨어를 설계하였다. 이 알고리즘의 하드웨어는 총 5개의 블록으로 가지고 있고 각각의 블록은 파이프라인 형태로 구성하고, 이는 Synopsys 툴을 이용하여 동작 및 성능을 검증하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.781-784
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• 1993

Most linguistic models of processes or plants known are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show two models for synchronous finite state machines (FSM) based on fuzzy logic, namely the Crisp-State-Fuzzy-Output (CSFO FSM) and Fuzzy-State-Fuzzy Output (FSFO FSM). As a result of the introduction of the FSM models, the improved architectures for fuzzy logic controller have been defined. These architectures featuring pipelined intelligent fuzzy controller are discussed in terms of dimensionality of the model. VLSI integrated circuit implementation issues of the fuzzy logic controller are also considered. The presented approach can be utilized for fuzzy controller hardware accelerators intended to work in the real-time environment.

• 한국항해학회지
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• 제22권2호
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• pp.47-52
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• 1998

Recently , there have been considerable researches about the fusion of fuzzy logic and neural networks. The propose of thise researches is to combine the advantages of both. After the function of approximation using GMDP (Generalized Multi-Denderite Product)neural network for defuzzification operation of fuzzy controller, a new fuzzy-neural network is proposed. Fuzzy membership function of the proposed fuzzy-neural network can be adjusted by learning in order to be adaptive to the variations of a parameter or the external environment. To show the applicability of the proposed fuzzy-nerual network, the proposed model is applied to a speed control o fDC sevo motor. By the hardware implementation, we obtained the desriable results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.981-984
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• 1993

During the past decade, several specific hardwares for fast fuzzy inference have been developed. Most of them are dedicated to a specific inference method and thus cannot support other inference methods. In this paper, we present a hardware architecture called KAFA(KAist Fuzzy Accelerator) which provides various fuzzy inference methods and fuzzy set operators. The architecture has SIMD structure, which consists of two parts; system control/interface unit(Main Controller) and arithmetic units(FPEs). Using the parallel processing technology, the KAFA has the high performance for fuzzy information processing. The speed of the KAFA holds promise for the development of the new fuzzy application systems.