• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.252-259
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection of a image. The FCS is based on the fuzzy logic system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. There are two different approaches, Michigan and Pittsburgh approaches, to acquire appropriate fuzzy rules by evolutionary computation. In this paper, we use the Michigan style in which a single fuzzy if-then rule is coded as an individual. Also the FCS employs the Genetic Algorithms to generate new rules and modify rules when performance of the system needs to be improved. The proposed method is evaluated by applying it to the edge detection of a gray-level image that is a pre-processing step of the computer vision. the differences of average gray-level of the each vertical/horizontal arrays of neighborhood pixels are represented into fuzzy sets, and then the center pixel is decided whether it is edge pixel or not using fuzzy if-then rules. We compare the resulting image with a conventional edge image obtained by the other edge detection method such as Sobel edge detection.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.10-18
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• 2003

In this paper, we proposed a learning algorithm for the ADALINE network. The proposed algorithm exploits fuzzy system for automatic tuning of the weight parameters of the ADALINE network. The inputs of the fuzzy system are error and change of error, and the output is the weight variation. We used different scaling factor for each weights. In order to verify the effectiveness of the proposed algorithm, we peformed the simulation and experimentation for the cases of the noise cancellation and the inverted pendulum control. The results show that the proposed algorithm does not need the learning rate and improves 4he performance compared to the Widrow-Hoff delta rule for ADALINE.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.111-117
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• 2010

To improve the performance of the prediction system, the system should reflect well the uncertainty of nonlinear data. Thus, this paper presents multiple prediction systems based on Type-2 fuzzy sets. To construct each prediction system, an Interval Type-2 TSK Fuzzy Logic System and difference data were used, because, in general, it has been known that the Type-2 Fuzzy Logic System can deal with the uncertainty of nonlinear data better than the Type-1 Fuzzy Logic System, and the difference data can provide more steady information than that of original data. Also, to improve each rule base of the fuzzy prediction systems, the HCBKA (Hierarchical Correlation Based K-means clustering Algorithm) was applied because it can consider correlationship and statistical characteristics between data at a time. Subsequently, to alleviate complexity of the proposed prediction system, a system selection method was used. Finally, this paper analyzed and compared the performances between the Type-1 prediction system and the Interval Type-2 prediction system using simulations of three typical time series examples.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.55-62
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• 2005

It is essential for a fuzzy logic controller to have an appropriate set of rules to perform at the desired level. The linguistic structure of the fuzzy logic controller allows a tentative linguistic policy to be used as an initial rule base. At the design stage, if one can reasonably assemble a good collection of rules, it may then be possible to be tuned to improve the controller performance. In this paper, we proposed the shifting method of rule base table to improve the performance of fuzzy controller. The proposed method is based on the principle of that the effect of the output to regulate the system would be greater when the error increases and the effect of output would be less when the error decreases. According to simulation results, it is an effective method to improve the fuzzy control rule base and the performance of fuzzy logic controllers.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.6
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• pp.625-632
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• 1992

This paper presents a new dc-motor position control approached by Variable Structure System. In order to eliminate a steady-state position error, we propose a switching function composed of position error, velocity, and current ripple. The switching function has an advantage compared to other ones. To determine the control signal voltage, we use a fuzzy logic method. The simulation results show expected performances.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.1
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• pp.10-18
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• 2000

This paper proposes a fuzzy logic cross-coupled controller using a new contouring modeling for a two-axis servo system. The general decoupled control approach may result in degraded contouring performance due to such factors as mismatch of axial dynamics and axial loop gains. In practice, such systems contain many uncertainties. The cross-coupled controller utilizes all axis position error information simultaneously to produce accurate contours. However, the conventional cross-coupled controllers cannot overcome friction, backlash, and parameter variations. Also since, it is difficult to obtain an accurate mathematical model of multi-axis system, here we investigate a fuzzy logic cross-coupled controller of servo system. In addition, new contouring error vector computation method is presented. The experimental results are presented to illustrate the performance of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.6
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• pp.69-75
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• 2003

This paper presents a fuzzy logic based approach to tracking control of a sampled nonlinear system. It is assumed that the plant to be controlled is under both the internal uncertainty and the external disturbances. Discrete-time adaptive fuzzy control method is proposed and its parameters are determined by the recently-spolighted convex optimization technique called LMI. Finally, the computer simulation is tarried out to verify the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.390-396
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• 1998

본 논문은 퍼지 논리 제어기(FLC)의 근사화 능력과 제어 성능을 동시에 향상시키는 정확한 무게 중심(Center Of Gravity; COG) 비퍼지화기를 제안한다. 본 논문은 비퍼지화 과정이 최적 선택의 한 과정이며 비퍼지화 방법의 적절한 선택이다. 제안한 COG 비퍼지화기의 정확성은 출력 소속 함수를 여러 개의 설계 파라메터(중신, 폭, 변경자(modifier))로 나타내고 이들 설계 파라메터들을 학습과 진화의 Lamarckian 상호 적응에 의하여 갱신함으로써 얻어진다. 이러한 학습과 진화의 상호 적응은 학습하지 않는 경우 보다 빠르게 최적 COG 비퍼지화기를 얻도록 하며, 보다 넓은 범위의 탐색으로최적해를 찾을 가능성을 높여 준다. 제안한 설계 방법은 목적 함수의 가중치를 조절하여 높은 근사화 능력, 높은 제어 성능, 또는 이들간의 균형된 성능을 갖는 다양한 특정 응용형(Application-specific)COG 비퍼지화기를 제공한다. 제안한 상호적응 COG 비퍼지화기의 설계방법을 트럭 후진 주차 제어 문제에 적용하여, 각각 시스템 오차와 평균 추적 거리로 나타내어진 근사화 능력과 제어 성능을 기존의 COG 비퍼지화기와 비교한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.2
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• pp.403-410
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• 1999

A conventional PID controller has poor performance when it applied to systems with unknown deadzones. To solve this problem, this paper proposes PID controller using two layered-fuzzy logic. The structure of controller is reconstructed with fuzzy compensator and fuzzy tuner on the conventional PID controller. Our proposed control scheme shows superior transient and steady-state performance compared to conventional PID controller. The scheme is robust to variations in deadzone nonlinearities as well as the steady-state gain of the plant. The performance of the developed controller is verified through simulation.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1849-1850
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• 2008

Type-2 퍼지 집합은 Type-1 퍼지 집합의 확장으로 Type-1 퍼지 집합으로는 다루기 힘든 언어적인 불확실성을 다루기 위해 고안되었다. 대표적인 퍼지 논리 시스템(Fuzzy Logic System; FLS)으론 Mamdani FLS 모델과 TSK FLS모델이 있다. 본 논문에서는 Interval Type-2 TSK FLS를 구성한다. FLS 구성을 위한 전반부는 가우시안 형태의 Type-2 멤버쉽 함수를 사용하며, 전.후반부 파라미터들은 오류역전파 알고리즘을 통한 학습으로 결정한다. 본 논문에서는 Type-1 TSK FLS와 Interval Type-2 TSK FLS를 설계하고 가스로 공정 데이터에 적용하여 성능을 비교 분석한다. 또한 노이즈를 추가한 데이터들을 통하여 노이즈에 대한 성능도 비교 분석한다.