• 제목/요약/키워드: TSK Rule

검색결과 22건 처리시간 0.021초

선박조타의 TSK 퍼지 비선형제어시스템 설계 (Design of TSK Fuzzy Nonlinear Control System for Ship Steering)

  • 채양범;이원창;강근택
    • 한국항해항만학회지
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    • 제26권2호
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    • pp.193-197
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    • 2002
  • 선박 조종방정식의 비선형 요소를 고려한 선박의 자동조타시스템의 제어기를 설계하기 위하여 TSK (Takagj-Sugeno-Kang) 퍼지 이론을 이용하였다. TSK 퍼지모델은 비선형 시스템을 매우 효율적으로 표현할 수 있으며, 또 TSK 퍼지모델은 결론부가 선형식으로 이뤄져 있어 체계적인 제어기 설계가 가능하다. 따라서 본 연구에서는 선박의 조종방정식을 TSK 퍼지모델로 표현하는 방법과 그 모델로부터 체계적으로 TSK 퍼지제어기를 설계하는 방법을 설명한다.

TSK퍼지 시스템의 안정도 해석 (Stability Analysis of TSK Fuzzy Systems)

  • 강근택;이원창
    • 한국지능시스템학회논문지
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    • 제8권4호
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    • pp.53-61
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    • 1998
  • 본 논문에서는 넓은 범위의 비선형 시스템들을 잘 표현할 수 있는 TSK(Takagai Sugeno Kang) 퍼지 시스템의 평형점의 지역 안정도를 해석하는 방법을 제시한다. TSK퍼지 모델은 TSK퍼지 규칙들로 구성되며, 각 규칙의 결론부는 상수항을 갖는 선형 입출력 방정식이다. TSK퍼지모델은 다수의평형점을 가질수 있으며, 각 평형점은 안정도에 있어서 역시 서로 단른 특징을 가질수 있다. 평형점의 지역 안정도는 평형점 부근에서 TSK퍼지 모델의 선형화로 얻어지는 자코비안 행렬의 교유치에 의해 결정된다. 본 논문에서는 연속시간 및 이산시간 시스템에 대한 안정도 해석을 위한 방법이 각각 제시된다.

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HCBKA 기반 오차 보정형 TSK 퍼지 예측시스템 설계 (Design of HCBKA-Based TSK Fuzzy Prediction System with Error Compensation)

  • 방영근;이철희
    • 전기학회논문지
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    • 제59권6호
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    • pp.1159-1166
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    • 2010
  • To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

HCBKA 기반 IT2TSK 퍼지 예측시스템 설계 (Design of HCBKA-Based IT2TSK Fuzzy Prediction System)

  • 방영근;이철희
    • 전기학회논문지
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    • 제60권7호
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    • pp.1396-1403
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    • 2011
  • It is not easy to analyze the strong nonlinear time series and effectively design a good prediction system especially due to the difficulties in handling the potential uncertainty included in data and prediction method. To solve this problem, a new design method for fuzzy prediction system is suggested in this paper. The proposed method contains the followings as major parts ; the first-order difference detection to extract the stable information from the nonlinear characteristics of time series, the fuzzy rule generation based on the hierarchically classifying clustering technique to reduce incorrectness of the system parameter identification, and the IT2TSK fuzzy logic system to reasonably handle the potential uncertainty of the series. In addition, the design of the multiple predictors is considered to reflect sufficiently the diverse characteristics concealed in the series. Finally, computer simulations are performed to verify the performance and the effectiveness of the proposed prediction system.

A Multiple Model Approach to Fuzzy Modeling and Control of Nonlinear Systems

  • Lee, Chul-Heui;Seo, Seon-Hak;Ha, Young-Ki
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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    • pp.453-458
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    • 1998
  • In this paper, a new approach to modeling of nonlinear systems using fuzzy theory is presented. So as to handle a variety of nonlinearity and reflect the degree of confidence in the informations about system, we combine multiple model method with hierarchical prioritized structure. The mountain clustering technique is used in partition of system, and TSK rule structure is adopted to form the fuzzy rules. Back propagation algorithm is used for learning parameters in the rules. Computer simulations are performed to verify the effectiveness of the proposed method. It is useful for the treatment fo the nonlinear system of which the quantitative math-approach is difficult.

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퍼지집합과 러프집합을 이용한 계층 구조 가스 식별 시스템의 설계 (Design of a Hierarchically Structured Gas Identification System Using Fuzzy Sets and Rough Sets)

  • 방영근;이철희
    • 전기학회논문지
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    • 제67권3호
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    • pp.419-426
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    • 2018
  • An useful and effective design method for the gas identification system is presented in this paper. The proposed gas identification system adopts hierarchical structure with two level rule base combining fuzzy sets with rough sets. At first, a hybrid genetic algorithm is used in grouping the array sensors of which the measured patterns are similar in order to reduce the dimensionality of patterns to be analyzed and to make rule construction easy and simple. Next, for low level identification, fuzzy inference systems for each divided group are designed by using TSK fuzzy rule, which allow handling the drift and the uncertainty of sensor data effectively. Finally, rough set theory is applied to derive the identification rules at high level which reflect the identification characteristics of each divided group. Thus, the proposed method is able to accomplish effectively dimensionality reduction as well as accurate gas identification. In simulation, we demonstrated the effectiveness of the proposed methods by identifying five types of gases.

Building a Fuzzy Model with Transparent Membership Functions through Constrained Evolutionary Optimization

  • Kim, Min-Soeng;Kim, Chang-Hyun;Lee, Ju-Jang
    • International Journal of Control, Automation, and Systems
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    • 제2권3호
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    • pp.298-309
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    • 2004
  • In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

데이터 전처리와 퍼지 논리 시스템을 이용한 전력 부하 예측 (Electric Load Forecasting using Data Preprocessing and Fuzzy Logic System)

  • 방영근;이철희
    • 전기학회논문지
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    • 제66권12호
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    • pp.1751-1758
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    • 2017
  • This paper presents a fuzzy logic system with data preprocessing to make the accurate electric power load prediction system. The fuzzy logic system acceptably treats the hidden characteristic of the nonlinear data. The data preprocessing processes the original data to provide more information of its characteristics. Thus the combination of two methods can predict the given data more accurately. The former uses TSK fuzzy logic system to apply the linguistic rule base and the linear regression model while the latter uses the linear interpolation method. Finally, four regional electric power load data in taiwan are used to evaluate the performance of the proposed prediction system.

A New Learning Algorithm for Neuro-Fuzzy Modeling Using Self-Constructed Clustering

  • Kim, Sung-Suk;Kwak, Keun-Chang;Kim, Sung-Soo;Ryu, Jeong-Woong
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 2005년도 ICCAS
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    • pp.1254-1259
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    • 2005
  • In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

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A New Learning Algorithm of Neuro-Fuzzy Modeling Using Self-Constructed Clustering

  • Ryu, Jeong-Woong;Song, Chang-Kyu;Kim, Sung-Suk;Kim, Sung-Soo
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • 제5권2호
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    • pp.95-101
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    • 2005
  • In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.