• Title/Summary/Keyword: Fuzzy factor

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Scale Factor Tuning of the Fuzzy Controller Using Continuous Fuzzy Input Variables (연속형 퍼지 입력변수를 사용하는 퍼지 제어기의 환산계수 동조)

  • Lim, Young-Cheol;Park, Jong-Gun;Wi, Seog-Oh;Jung, Hyun-Cheol
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.1359-1361
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    • 1996
  • This paper describes a design of real time fuzzy controller using Minimum fuzzy control Rule Selection Method(MRSM). The control algorithm of dynamic systems needs less computation time and memory. To reduce the computation time of fuzzy logic controller, minimum number of rules are to be selected for the fuzzy input variable. The universe of discourse is divided by the number of linguistic labels to allocate the assigned membership function to the fuzzy input variables. In this case, since fuzzy input variables are continuous, scale factor SU is tuned independently. According to increment of SU control surface is improved to adapt the change of system parameter. At this, crisp control surface is increased. With the increament of crisp control surface, fuzzy control surface is reduced. When error state deviates from desirable error state, crisp control surface is more useful than fuzzy control surface for obtaining fast rising time.

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FUZZY IDENTIFICATION BY MEANS OF AUTO-TUNING ALGORITHM AND WEIGHTING FACTOR

  • Park, Chun-Seong;Oh, Sung-Kwun;Ahn, Tae-Chon;Pedrycz, Witold
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.06a
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    • pp.701-706
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    • 1998
  • A design method of rule -based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of " IF..., THEN,," statements. using the theories of optimization and linguistic fuzzy implication rules. The improved complex method, which is a powerful auto-tuning algorithm, is used for tuning of parameters of the premise membership functions in consideration of the overall structure of fuzzy rules. The optimized objective function, including the weighting factors, is auto-tuned for better performance of fuzzy model using training data and testing data. According to the adjustment of each weighting factor of training and testing data, we can construct the optimal fuzzy model from the objective function. The least square method is utilized for the identification of optimum consequence parameters. Gas furance and a sewage treatment proce s are used to evaluate the performance of the proposed rule-based fuzzy modeling.

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Fuzzy Control Method By Automatic Scaling Factor Tuning (자동 양자이득 조정에 의한 퍼지 제어방식)

  • 강성호;임중규;엄기환
    • Proceedings of the IEEK Conference
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    • 2003.07c
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    • pp.2807-2810
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    • 2003
  • In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

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PD+I Fuzzy Controller Using Error-Accumulating Applying Factor (오차적분 적용계수를 이용한 PD+I 퍼지제어기)

  • Chun, Kyung-Han;Lee, Yun-Jung;Park, Bong-Yeol
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.3
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    • pp.193-198
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    • 2002
  • In this paper, we Propose a PD+I fuzzy controller using an error-accumulating applying factor. In fuzzy control, analytical study was done formerly, in which fuzzy control can be classified by PD type and PI type, and also the study for getting merits of both types was done, too. But the mixed type has a complex structure and many parameters. The proposed fuzzy controller is 2-input 2-out-put and PD type fuzzy control is used as a basic structure. And the proposed controller annihilates a steady-state error and improves transient responses because of using the error-accumulating applying factor which is determined in the real time along the current state of controlled process. Futhermore it is easy to tune the system because of decreasing the number of scaling factors and the I type controller with resetting resolves the integral wind-up problem. Finally we apply the proposed scheme to various plants and show the performance betterment.

Improved Self-tuning Fuzzy PID Controller (향상된 자기동조 퍼지 PID 제어기)

  • Roh, Jae-Sang;Lee, Young-Seog;Suh, Bo-Hyeok
    • Proceedings of the KIEE Conference
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    • 1994.11a
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    • pp.338-341
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    • 1994
  • This paper presents a Fuzzy-PID controller based on Fuzzy logic. Up to now PID controller has had the difficulty of obtaining the optimal gain, and Fuzzy controller has had the difficulty of determining scale factor affecting the performance of control. So that a Fuzzy-PID controller is presented here self tuning of the scale factor and optimal gain. The results of simulation show a good performance in comparison with Ziegler-Nichols controller, having the generality of determining the components of scale factor in Fuzzy rule.

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Fuzzy Power Factor Control Systems

  • Cho, Seong-Won;Kim, Jae-Min;Jung, Jae-Yoon;Lim, Cheol-Su
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.1
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    • pp.45-49
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    • 2004
  • A method for obtaining the power energy with high quality is to keep the power factor for a load as close to unity as feasible. In this paper, we present a new method to improve the power factor for a load. The proposed method uses fuzzy control techniques in order to determine how many parallel capacitors are to be connected to the load for the correction of the power factor.

Fuzzy-Bayes Fault Isolator Design for BLDC Motor Fault Diagnosis

  • Suh, Suhk-Hoon
    • International Journal of Control, Automation, and Systems
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    • v.2 no.3
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    • pp.354-361
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    • 2004
  • To improve fault isolation performance of the Bayes isolator, this paper proposes the Fuzzy-Bayes isolator, which uses the Fuzzy-Bayes classifier as a fault isolator. The Fuzzy-Bayes classifier is composed of the Bayes classifier and weighting factor, which is determined by fuzzy inference logic. The Mahalanobis distance derivative is mapped to the weighting factor by fuzzy inference logic. The Fuzzy-Bayes fault isolator is designed for the BLDC motor fault diagnosis system. Fault isolation performance is evaluated by the experiments. The research results indicate that the Fuzzy-Bayes fault isolator improves fault isolation performance and that it can reduce the transition region chattering that is occurred when the fault is injected. In the experiment, chattering is reduced by about half that of the Bayes classifier's.

Fuzzy Logic Based Extended Integral Control for Load Frequency Control (부하 주파수 제어를 위한 퍼지 로직 기반 확장 적분 제어)

  • Ryu, Heon-Su;Lee, Jong-Gi;Kim, Seog-Joo;Kim, Baik;Moon, Young-Hyun
    • Proceedings of the KIEE Conference
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    • 2001.05a
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    • pp.210-213
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    • 2001
  • This study presents an effective variable forgetting factor method based on fuzzy logic to suppress frequency droop in extended integral load frequency control. The performance of the extended integral control is greatly dependent on the decaying factor. For an optimal or near optimal performance, it is necessary that the decaying factor as well as the feedback gains should be changed very quickly in response to changes in the system dynamics. However, because of its time-varing characteristic, the optimal decaying factor is difficult to be selected analytically. By adopting fuzzy set theory, the decaying factor can be determined quickly to respond to the variation of the feedback signals. This study builds a fuzzy rule base with use of the change of frequency and its rate as inputs. The computer simulation has been conducted for the single machine system. The simulation results show that the proposed fuzzy 1o81c based controller yields more improved control performance than the conventional PI controller.

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PD+I-type fuzzy controller using Simplified Indirect Inference Method

  • Kim, Ji-Hoon;Jeon, Hae-Jin;Chun, Kyung-Han;Park, Bong-Yeol
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.179.5-179
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    • 2001
  • Generally, while PD-type fuzzy controller has good performance in transient period, it has uniform steady state error of response. To improve limitations of PD-type fuzzy controller, we propose a new fuzzy controller to improve the performance of transient response and to eliminate the steady state error of response. In this paper, PD-type fuzzy controller is used a simplified indirect inference method(SIIM). When the SIIM is applied, the proposed method has the capability of the high speed inference and adapting with increasing the number of the fuzzy input variables easily. The outputs of this controller are the output calculated by PD-type fuzzy controller and the accumulated error scaling factor. Here, the accumulated error scaling factor is adjusted by fuzzy rule according to the system state variables. To show the usefulness of the proposed controller, it is applied to 0-type 2nd-order linear system.

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Development of Classification Model Using Neural Network (신경회로망을 이용한 분류모형 개발)

  • Park, Kwang-Bak;Park, Young-Man;Hwang, Seung-Gook
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.5
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    • pp.638-641
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    • 2008
  • In this paper, a model to classify the method using the fuzzy TAM with preprocessing of data was developed. The preprocessing method can be divide the problem using the characteristics in the case of category type factor. In case of continuous type factor, if there was exist factor's range which is not overlapping by class, the data belong to the range was fixed and eliminated in classification. After these preprocessing of data, classified operation of Fuzzy TAM is performed.