• Journal of the Institute of Convergence Signal Processing
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• v.4 no.2
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• pp.12-17
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• 2003

This paper proposes content-based image retrieval system with fuzzy ART neural network algorithm. Retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster huge image data pertinently, Because current retrieval methods using similarities have several problems like low accuracy of retrieving and long retrieval time, a solution is necessary to complement these problems. This paper presents a content-based image retrieval system with neural network in order to reinforce abovementioned problems. The retrieval system using fuzzy ART algorithm normalizes color and texture as feature values of input data between 0 and 1, and then it runs after clustering the input data. The implemental result with 300 image data shows retrieval accuracy of approximately 87%.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.8
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• pp.1195-1200
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• 2013

In this paper, we proposed an enhanced Max-Min neural network by auto-tuning of learning rate using fuzzy control method. For the reduction of training time required in the competition stage, the method was proposed that arbitrates dynamically the learning rate by applying the numbers of the accuracy and the inaccuracy to the input of the fuzzy control system. The experiments using real concrete crack images showed that the enhanced Max-Min neural network was effective in the recognition of direction of the extracted cracks.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.187-192
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• 2008

A saturation and deadzone compensator is designed for robot systems using fuzzy logic (FL) and neural network (NN). The classification property of FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is simulated on a robot system to show its efficacy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.383-388
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• 1999

In this paper, we propose the fuzzy-neural controller with genetic algorithm(GA) for precise on-line control. We design the proposed controller having a ability to adjust membership function for a plant by advanced algorithm of fuzzy-neural network after approximative one being completed by genetic algorithm. Finally we compare the result for a speed control of DC servo motor by the proposed controller with GA-fuzzy one in order to evaluate its performance and precision.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.536-540
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• 2003

최근에 개인의 경험을 통해 얻어지는 외부의 물리적 자극에 대한 복합적인 감성을 측성 및 분석하여 공학적으로 처리함으로서 인간이 보다 편리하고 안락한 생활을 영위하도록 하는 연구가 활발히 진행되고 있다. 본 논문에서는 색채 심리를 바탕으로 한 감성을 인식할 수 있는 생리학적 퍼지 신경망은 제안하였다. 본 논문에서 제안한 생리학적 퍼지 뉴런 구조를 기반으로 하여 입력층, 퍼지 귀속 시넵스(Fuzzy Membership Synapse) 및 출력층으로 구성되며 지도 학습(supervised learning)으로 동작된다. 제안된 생리학적 퍼지 신경망을 단일 색상 정보에 따른 감성 인식에 적용한 결과, 단일 색상 정보에 따른 감성 인식에 있어서 효율적임을 확인 할 수 있었다.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1473-1482
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• 1996

This study examines the response characteristics of the fuzzy controller and the fuzzy neural network controller. The former is excellent in terms of the overshoot at its values and has great advantages on the disturbance. But there exist some errors in its desired output. Many methods have been introduced that remove the errors of the desired state. This study is in more favor of the fuzzy neural network controller using the neural network than any other method. The fuzzy neural network controller complements the shortcomings of fuzzy controller and can be an accurate controller by being well-without any disturbance or error-converged to the desired output. And it is through simulation that the comparison of the two controllers is carried out in this study.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.43-54
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• 1997

Knowledge acquisition is one of the most difficult problems in designing fuzzy systems. As application domains of fuzzy systems become larger and more complex, it is more difficult to find the relations among the system's input- outpiit variables. Moreover, it takes a lot of efforts to formulate expert's knowledge about complex systems' control actions by linguistic variables. Another difficulty is to define and adjust membership functions properly. Soin conventional fuzzy systems, the membership functions should be adjusted to improve the system performance. This is time-consuming process. In this paper, we suggest a new approach to design a fuzzy system. We design a fuzzy system using two neural networks, Kohonen neural network and backpropagation neural network, which generate fuzzy rules automatically and construct inference network. Since fuzzy inference is performed based on fuzzy relation in this approach, we don't need the membership functions of each variable. Therefore it is unnecessary to define and adjust membership functions and we can get fuzzy rules automatically. The design process of fuzzy system becomes simple. The proposed approach is applied to a simulated automatic car speed control system. We can be sure that this approach not only makes the design process of fuzzy systems simple but also produces appropriate inference results.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.54-60
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• 2002

In this paper, we proposed a hybrid neural network-fuzzy controller which compensate a output of neural network controller. Even if learn by neural network controller, it can occur an bad results from disturbance or load variations. So in order to adjust above case, we used the fuzzy compensator to get an expected results. And the weight of main neural network can be changed with the result of loaming a inverse model neural network of Plant, so a expected dynamic characteristics of plant can be got. As the results of simulation through the second order plant, we confirmed that the proposed speed controller get a good response compare with a neural network controller. We implemented the controller using the DSP processor and applied in a hydraulic servo system. And then we observed an experimental results.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.313-319
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• 2007

In this paper, we propose a novel method to extract an area of car licence plate and codes of vehicle number from a photographed car image using features on vertical edges and a new Fuzzy neural network algorithm to recognize extracted codes. Prewitt mask is used in searching for vertical edges for detection of an area of vehicle number plate and feature information of vehicle number palate is used to eliminate image noises and extract the plate area and individual codes of vehicle number. Finally, for recognition of extracted codes, we use the proposed Fuzzy neural network algorithm, in which FCM is used as the learning structure between input and middle layers and Max_Min neural network is used as the learning structure within inhibition and output layers. Through a variety of experiments using real 150 images of vehicle, we showed that the proposed method is more efficient than others.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2152-2154
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• 2002

신경망을 이용한 적응제어는 학습능력에 따라 외란작용에 스스로 대처하고, 정밀한 제어가 가능하지만 학습파라미터가 최적화되기 전에는 불안정한 제어응답을 보인다. 퍼지논리는 전문가의 경험을 논리화한 것으로 제어특성은 좋으나, 외란에 대한 적응력이 부족하여 계속적인 오프셋이 발생할 수 있다. 따라서, 퍼지와 신경망을 시스템의 동특성에 따라 혼용한 제어방식을 제시하고, 시뮬레이션으로 시간지연이 있는 CSTH의 온도와 비선형 공정인 pH 중화공정에 적용하여 단순신경망 제어어보다 개선된 제어응답 특성을 얻었다.