• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1626-1629
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• 2002

This paper describes novel methods to construct fuzzy inference rules with gradient descent. The present methods have a constructive mechanism of the rule unit that is applicable in two parameters: the central value and the width of the membership function in the antecedent part. The first approach is to create the rule unit at the nearest position from the input space, for the central value of the membership function in the antecedent part. The second is to create the rule unit which has the minimum width, for the width of the membership function in the antecedent part. Experimental results are presented in order to show that the proposed methods are effective in difference on the inference error and the number of learning iterations.

• 수산해양기술연구
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• 제36권1호
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• pp.54-59
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• 2000

중층트롤어구의 수심 제어는 시스템의 복잡성과 비선형성 등으로 인하여 아직까지 자동화되지 않았다. 본 연구에서는 회류수조에서 작동되는 모형 트롤어구의 예망시스템을 제작하였으며, 이 시스템의 수심을 자동으로 제어하기 위해서 퍼지논리를 이용한 제어시스템을 구성하여 성능을 실험하였다. 제어시스템의 수심제어 규칙은 숙련된 항해사나 선장이 실제 조업에서 어구의 수심을 제어하기 위해 사용하는 지식을 제어규칙화 한 것과 모형실험에 적합하도록 수정한 규칙 두 가지를 사용하였다. 제어계의 성능은 예망속도를 일정히 유지하면서 목표수심을 스텝상으로 변경시켰을 때의 추종성능 실험과 목표수심을 일정히 유지하면서 예망속도를 변경시켰을 때의 보상성능을 실험을 통하여 분석하였다. 1. 본 연구에서 제안된 두 가지 제어기는 모두 일정한 유속(0.35m/s)에서 스텝상의 목표수심 변경에대해서 빠른 추종성능을 나타내었다. 특히 수정된 제어규칙에서는 모형 어구의 수십을 보다 안정되게 제어하였다. 2. 예망속도(유속)를 변화시켜 어구저항을 증감시킨 실험에서도 두 제어기는 비교적 양호한 보상 성능을 나타내었는데, 실제 조업에서 사용하는 규칙은 작은 외란에도 빨리 반응하였으며, 수정된 제어규칙은 수심편차가 어느 정도 커져야 제어동작을 하였다. 3. 본 연구에서 제작된 모형트롤시스템은 실물트롤 시스템의 운동 특성과 거의 일치하였고, 또한 설계된 제어기는 양호한 제어성능을 나타내어 모형실험을 통한 시스템의 해석과 실물 트롤시스템에 적용가능성이 높은 제어계의 설계가 가능하였다

• 방송공학회논문지
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• 제5권1호
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• pp.123-129
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• 2000

본 논문에서는 장거리 통신시스템의 적응 반향제거기에서 퍼지제어에 기초한 새로운 동시통화 검출방법을 제안하였다. 이 방법에서, 동시통화 검출을 위한 퍼지추론의 두 입력 변수는 근단 신호와 실제 반향 신호가 더해진 요소 신호와 에러 신호 사이의 상호상관 계수 그리고 소요신호와 추정된 신호 사이의 상호산관계수를 사용하였다. 퍼지 제어기에서 사다리꼴 소속함수로 퍼지화하고 추론규칙을 이용하여 합성하였으며, 합성된 결과를 무게 중심법에 의하여 디퍼지화한 값으로 동시통화와 반향경로 변화를 검출하도록 하였다. 제안한 통화 검출기는 기존의 알고리듬보다 동시통화와 반향경로 변화를 잘 추정하는 것을 보였다.

• 제어로봇시스템학회논문지
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• 제19권9호
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• pp.839-845
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• 2013

A CSTR (Continuous Stirred Tank Reactor) is a highly nonlinear process with varying parameters during operation. Therefore, tuning of the controller and determining the transition policy of controller parameters are required to guarantee the best performance of the CSTR for overall operating regions. In this paper, a methodology employing the 2DOF (Two-Degree-of-Freedom) PID controller, the anti-windup technique and a fuzzy gain scheduler is presented for the temperature control of the CSTR. First, both a local model and an EA (Evolutionary Algorithm) are used to tune the optimal controller parameters at each operating region by minimizing the IAE (Integral of Absolute Error). Then, a set of controller parameters are expressed as functions of the gain scheduling variable. Those functions are implemented using a set of "if-then" fuzzy rules, which is of Sugeno's form. Simulation works for reference tracking, disturbance rejecting and noise rejecting performances show the feasibility of using the proposed method.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 추계학술대회 학술발표 논문집
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• pp.191-196
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• 1998

A new Fuzzy Logic Controller(FLC) called a Gradient-Descent Based Self-Organizing Controller is presented. The Self-Organizing Controller(SOC) has two inputs such as error and change of error, and updates control rules with monitoring a performance measure. There are many works in the SOC which concentrate on the self-organizing ability in control rule base, but have a few research on the performance measure which is akin to sliding mode control. With this procedure, we can get a robust performance measure on the SOC. To verify the perfomance of proposed controller, we have performed for the cart-pole system which is one of the well-known benchmark problem in the control literature.

• International Journal of Control, Automation, and Systems
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• 제3권2호
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• 한국지능시스템학회논문지
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• 제24권1호
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• pp.102-109
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• 2014

TSK 퍼지 시스템의 뛰어난 성능을 일반 산업 현장에서 가장 많이 사용되고 있는 PID 제어기에 접목시켜, 비선형 시스템의 제어가 가능하고 강인성이 뛰어난 퍼지 PID 제어기의 설계를 제안한다. TSK 퍼지 제어기는 TSK 퍼지 모델로부터 극 배치법을 이용하여 설계되며, 비선형 시스템의 제어에서 시스템의 응답이 원하는 응답과 같아지도록 하는 뛰어난 능력이 있으나 구조가 복잡하여 산업 현장에서 사용되기에는 어려움이 있다. 본 연구에서는 구현하기 간편한 PID 제어기의 형태를 하면서, TSK 퍼지 제어기의 도움을 받아 설계되는 퍼지 PID 제어기를 제안한다. 즉, 먼저 비선형 시스템의 TSK 퍼지 제어기를 설계하고 그 TSK 퍼지 제어기의 제어 시뮬레이션으로부터 얻은 데이터를 이용하여 제안하는 퍼지 PID 제어기를 설계한다. 제안하는 제어기를 연속시간 비선형 시스템과 이산시간 비선형 시스템의 예제에 적용시켜 제어 시뮬레이션을 하였다. 그 결과 기존의 선형 PID 제어기로는 제어가 원만하지 않았으나 제안하는 제어기로는 원하는 응답 형태와 거의 같은 응답을 보이는 제어가 가능함을 알 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1315-1318
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• 2004

Based on fuzzy 2-mean classification and template matching method, we propose a new algorithm to detect the edges of an image. In the algorithm, fuzzy 2-mean classification can classify all pixels in the mask into two clusters whatever the mask in the dark or light region; and template matching not only determines the edge's direction, but also thins the detected edge by a set of inference rules and, by the way, reduces the impulse noises.

• 한국지능시스템학회논문지
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• 제7권5호
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• pp.14-20
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• 1997

This paper proposes a new design method for the SMC-type FLC and shows that a SMC-type LFC is an extension of the SMC with BL. The conventional SMC-type FLC uses error and change-of-error as inputs of the FLC and generates the absolute value of a switching magnitude. Then, the fuzzy rule table is constructed on a two-dimensional space of the phase plane and has commonly the skew symmetric property. In this paper, we introduce a new variable, signed distance, from the skew symmetric property of the rule table. And thd variable becomes only a fuzzy variable that is used to generate the control input of a SMC-type FLC. that is, we design a new SMC-type FLC that uses a signed distance and a control input as the variables representing the contents of the rule-antecedent and the rule-con-sequent, respectively. Then the number of total rules is reduced and the control performance is almost the same as that of the conventional SMC-type FLC. Additionally, we derive the control law of the ordinary SMC with BL from a new SMC-type FLC. Namely, we show that a FLC is an extension of the SMC with BL.