• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.583-586
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• 1995

In this paper, a nor fuzzy control algorithm for the target tracking system is proposed, and its characteristics are analyzed and compared with those of the traditional PID controller. Fuzzy rules are generated experimentally using Mamdani's minimum operation and the center of area method. The experimental results prove that the proposed fuzzy algorithm is excellent in our tracking system and its performance is superior to that of the PID controller.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.3
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• pp.298-305
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• 2015

This paper provides experimental results of an excitation control system of the synchronous generator on board ships in accordance with rules of classification society to make sure its performance. The experiment compares and reviews control results between PID control and fuzzy logic control applied to change of loads of the generator in order to make sure to satisfy the rules of classification society. Both of them are written by Labview program. In case of PID Control, this paper firstly adjusts the gains by ultimate sensitive method and the gains is more tuned by engineer's experience. And the fuzzy logic controller uses Mamdani method to make membership function for error between reference voltage and measuring voltage, differential error rate and output voltage. This paper is to make sure the experimental results of the proposed excitation control system applied to actual small synchronous generator with PID control and fuzzy logic written by using Labview program and it is proved on stability and improvement through experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.64-70
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• 2014

In this paper, we propose a method that gives motion command to a mobile robot to recognize human being's hand gesture. Former way of the robot-controlling system with the movement of hand used several kinds of pre-arranged gesture, therefore the ordering motion was unnatural. Also it forced people to study the pre-arranged gesture, making it more inconvenient. To solve this problem, there are many researches going on trying to figure out another way to make the machine to recognize the movement of the hand. In this paper, we used third-dimensional camera to obtain the color and depth data, which can be used to search the human hand and recognize its movement based on it. We used HMM method to make the proposed system to perceive the movement, then the observed data transfers to the robot making it to move at the direction where we want it to be.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.6
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• pp.842-848
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• 2008

This paper deal with uncertainty problem by using Type-2 fuzzy logic set for nonlinear system modeling. We design Type-2 fuzzy logic system in which the antecedent and the consequent part of rules are given as Type-2 fuzzy set and also analyze the performance of the ensuing nonlinear model with uncertainty. Here, the apexes of the antecedent membership functions of rules are decided by C-means clustering algorithm and the apexes of the consequent membership functions of rules are learned by using back-propagation based on gradient decent method. Also, the parameters related to the fuzzy model are optimized by means of particle swarm optimization. The proposed model is demonstrated with the aid of two representative numerical examples, such as mathematical synthetic data set and Mackey-Glass time series data set and also we discuss the approximation as well as generalization abilities for the model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.738-745
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• 2001

In this paper, when the moving object move to the three-dimentional space, the tracking system track the moving object using the fuzzy reasoning. The joint angle el of the manipulator rotate from $0^{\circ}\; to\; 360^{\circ}$ , and the joint angle $\theta_2$rotate from$0^{\circ}\; to\; 360^{\circ}$. The fuzzy singleton is used for fuzzification and the control rule is twenty five and the fuzzy inference method is simplified Mamdani's reasoning and the defuzzification is the SCOG(Simplified Center Of Gravity) of the fuzzy controller To measure of the performance of the designed system, the fuzzy controller is compared with the CTM(Computed Torque Method) controller at the same condition. when the disturbance torque is ON, the both of CTM and fuzzy controller tracked object without error, However, the disturbance torque changed 0.4N, the CTM controller is 10 times greater than fuzzy controller at the sum of absolute error difference. The designed system is showed it's robustness against with disturbance.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.776-782
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• 2004

Generally, Max-Min CRI (Compositional Rule of Inference ) method by Zadeh and Mamdani is used in the conventional fuzzy inference. However, owing to the problems of Max-Min CRI method, the inference often results in significant error regions specifying the difference between the desired outputs and the inferred outputs. In this paper, I propose a New Max-Min CRI method which can solve some problems of the conventional Max-Min CRI method. And then this method is simulated in a D.C.series motor, which is a bench marking system in control systems, and showed that the new method performs better than the other fuzzy inference methods.

• Journal of Industrial Technology
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• v.17
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• pp.323-330
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• 1997

In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. To express the various and complex behavior of nonlinear system, we combine multiple model method with hierachical prioritized structure, and the mountain clustering technique is used in partitioning of system. TSK rule structure is adopted to form the fuzzy rules, and Back propagation algorithm is used for learning parameters in consequent parts of the rules. Also we soften the paradigm of Mamdani's inference mechanism by using Yager's S-OWA operators. Computer simulations are performed to verify the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.359-365
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• 2008

CP(Counterpropagation)알고리즘은 서로 다른 두 개의 신경망이 하나로 결합 된 혼합형 모델로서, 다른 신경망 모델에 비해 비교적 단순하고 빠른 학습 속도를 보인다. 그러나 CP 알고리즘은 다양한 패턴이 입력되면 충분한 경쟁층의 수가 설정되지 않아 학습이 불안정하고, 출력층에서 연결강도를 조정할 때 일반적인 학습률 조정방법으로 불안정한 학습 결과를 보인다. 이러한 문제점을 해결하기 위해 다수의 경쟁층을 설정하여 경쟁층에서 패턴 분류의 정확성을 높이고, 입력 벡터와 승자 뉴런의 대표 벡터간의 차이와 승자 빈도수를 반영하여 학습률을 동적으로 조정하여 경쟁층에서의 학습이 안정적으로 진행되도록 하고, 출력층에서 연결강도를 조정할 때 모멘텀(momentum)학습법을 적용한 개선된 CP 알고리즘이 제안되었다. 본 논문에서는 개선된 CP 알고리즘에서 경쟁층의 수를 효율적으로 설정하기 위해 퍼지 제어 기법을 이용하여 경쟁층의 수를 결정하는 방법을 제안한다. 제안된 방법은 CP 알고리즘에 입력되는 패턴의 정보를 이용하여 퍼지 소속 함수를 설계하고 입력에 대한 소속도를 계산한 후, 퍼지 제어 규칙을 적용하고, Mamdani의 Min_Max 추론 방법으로 추론한다. 퍼지 추론을 통해 최종적으로 얻어진 값을 무게 중심법으로 비퍼지화 하여 최종적으로 개선된 CP 알고리즘의 경쟁층의 수를 결정하는데 적용한다. 제안된 방법의 학습 및 인식 성능을 평가하기 위해, 숫자, 영어 등과 같이 다양한 패턴을 실험에 적용한 결과, 제안된 방법이 경쟁층의 수를 결정하는데 효과적임을 확인할 수 있었다.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.938-946
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• 1992

A novel 2-dimensional matched filter of the parallel-jaw type using fuzzy logic is proposed for bin picking. Specifically, the averaged pixel intensity of the windowed region for the filtering is considered to be fuzzy. Also membership functions for darkness and brightness are designed by employing the intensity histogram of the image. Then a rule is given to know how much a windowed region can be a possible holdsite. Furthermore eight rules are made to determine the part orientation, where Mamdani's reasoning method is applied. The proposed technique shows better performances than that of the conventional matched filtering technique in the following senses` 1) most of holdsites determined by the proposed technique are not concentrated at the locations nearly the end of part and 2) our filter is rather insensitive to noises than the conventional method. To show the validities of our proposed technique, some experimental results are illustrated and compared with the results by conventional matched filter technique.

• Journal of Advanced Navigation Technology
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• v.12 no.4
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• pp.378-383
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• 2008

This paper presents a method of image analysis based on discrete cosine transform (DCT) and fuzzy inference(Fl). It concentrated not only on the design of fuzzy inference algorithm but also on incorporating human visual parameter(HVP) into transform coefficients. In the first, HVP such as entropy, texture degree are calculated from the coefficients matrix of DCT. Secondly, using these parameters, fuzzy input variables are generated. Mamdani's operator as well as ${\alpha}$-cut function are involved to simulate the proposed approach, and consequently, experimental results are presented to testify the performance and applicability of the proposed scheme.