• Title/Summary/Keyword: Fuzzy Rules

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Design of Fuzzy Neural Networks Based on Fuzzy Clustering and Its Application (퍼지 클러스터링 기반 퍼지뉴럴네트워크 설계 및 적용)

  • Park, Keon-Jun;Lee, Dong-Yoon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.1
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    • pp.378-384
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    • 2013
  • In this paper, we propose the fuzzy neural networks based on fuzzy c-means clustering algorithm. Typically, the generation of fuzzy rules have the problem that the number of fuzzy rules exponentially increases when the dimension increases. To solve this problem, the fuzzy rules of the proposed networks are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the learning of fuzzy neural networks is realized by adjusting connections of the neurons, and it follows a back-propagation algorithm. The proposed networks are evaluated through the application to nonlinear process.

Implementation of the Arrangement Algorithm for Autonomous Mobile Robots (자율 이동 로봇의 정렬 군지능 알고리즘 구현)

  • Kim, Jang-Hyun;Kong, Seong-Gon
    • Proceedings of the KIEE Conference
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    • 1998.07g
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    • pp.2186-2188
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    • 1998
  • In this paper, Fundamental rules governing group intelligence "arrangement" behavior of multiple number of autonomous mobile robots are represented by a small number of fuzzy rules. Complex lifelike behavior is considered as local interactions between simple individuals under small number of fundamental rules. The fuzzy rules for arrangement are generated from clustering the input-output data obtained from the arrangement algorithm. Simulation shows the fuzzy rules successfully realizes fundamental rules of the flocking group behavior.

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Implementation of the Obstacle Avoidance Algorithm of Autonomous Mobile Robots by Clustering (클러스터링에 의한 자율 이동 로봇의 장애물 회피 알고리즘)

  • 김장현;공성곤
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1998.10a
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    • pp.504-510
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    • 1998
  • In this paper, Fundamental rules governing group intelligence "obstacle avoidance" behavior of multiple autonomous mobile robots are represented by a small number of fuzzy rules. Complex lifelike behavior is considered as local interactions between simple individuals under small number of fundamental rules. The fuzzy rules for obstacle avoidance are generated from clustering the input-output data obtained from the obstacle avoidance algorithm. Simulation shows the fuzzy rules successfully realizes fundamental rules of the obstacle avoidance behavior.

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Optimazation of Simulated Fuzzy Car Controller Using Genetic Algorithm (유전자 알고즘을 이용한 자동차 주행 제어기의 최적화)

  • Kim Bong-Gi
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.10 no.1
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    • pp.212-219
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    • 2006
  • The important problem in designing a Fuzzy Logic Controller(FLC) is generation of fuzzy control rules and it is usually the case that they are given by human experts of the problem domain. However, it is difficult to find an well-trained expert to any given problem. In this paper, I describes an application of genetic algorithm, a well-known global search algorithm to automatic generation of fuzzy control rules for FLC design. Fuzzy rules are automatically generated by evolving initially given fuzzy rules and membership functions associated fuzzy linguistic terms. Using genetic algorithm efficient fuzzy rules can be generated without any prior knowledge about the domain problem. In addition expert knowledge can be easily incorporated into rule generation for performance enhancement. We experimented genetic algorithm with a non-trivial vehicle controling problem. Our experimental results showed that genetic algorithm is efficient for designing any complex control system and the resulting system is robust.

Acquisition of Fuzzy Control Rules using Genetic Algorithm for a Ball & Beam System

  • S.B. Cho;Park, K.H.;Lee, Y.W.
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.40.6-40
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    • 2001
  • Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

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Design of Dual Fuzzy Logic Controller using $e-{\Delta}e$ Phase Plane for Hydraulic Servo Motor (유압 서보 모터를 위한 $e-{\Delta}e$ 위상평면을 이용한 이중 퍼지 로직 제어기 설계)

  • Shin, Wee-Jae;Moon, Jeong-Hoon
    • Journal of the Institute of Convergence Signal Processing
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    • v.8 no.3
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    • pp.222-226
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    • 2007
  • In this paper we composed the dual fuzzy rules using each region of specific points and $e-{\Delta}e$ phase plane In order to make dual fuzzy rule base. We composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. proposed method is alternately use at specific points of $e-{\Delta}e$ phase plane with two fuzzy control rules that is one control rule occruing the steady state error in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Also, two fuzzy control rules in the $e-{\Delta}e$ phase plane decide the change time according to response characteristics of plants. In order to confirm thef proposed algorithm. As the results of experiments through the hydraulic servo motor control system with a DSP processor, We verified that proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

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Structurally Adaptive Fuzzy Radial Basis Function Networks (구조적으로 적응하는 퍼지 RBF 신경회로망)

  • Choi, Jong-Soo;Lee, Gi-Bum;Kwon, Oh-Shin
    • Proceedings of the KIEE Conference
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    • 1998.07g
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    • pp.2203-2205
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    • 1998
  • This paper describes fuzzy radial basis function networks(FRBFN) extracting fuzzy rules through the learning from training data set. The proposed FRBFN is derived from the functional equivalence between RBF networks and fuzzy inference systems. The FRBFN learn by assigning new fuzzy rules and updating the parameters of existing fuzzy rules. The parameters of the FRBFN are adjusted using the standard LMS algorithm. The performance of the FRBFN is illustrated with function approximation and system identification.

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A Study on Genetic Algorithms for Automatic Fuzzy Rule Generation

  • Cho, Hyun-Joon;Wang, Bo-Hyeum
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1996.10a
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    • pp.275-278
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    • 1996
  • The application of genetic algorithms to fuzzy rule generation holds a great deal of promise in overcoming difficult problems in fuzzy systems design. There are some aspects to be considered when genetic algorithms are used for generating fuzzy rules. In this paper, we will present an aspect about the control surface constructed by the resultant rules. In the extensive simulations, an important observation that the rules searched by genetic algorithms are randomly scattered is made and a solution to this problem is provided by including a smoothness cost in the objective function. We apply the fuzzy rules generated by genetic algorithms to the fuzzy truck backer-upper control system and compare them with the rules made by an expert.

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GMDH by Fuzzy If-Then Rules with Certainty Factors

  • M.Balazinski;Katsunori-Yokode;Hisao-Ishibuchi
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 1993.06a
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    • pp.802-805
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    • 1993
  • A method of automatic learning of fuzzy if-then rules with certainty factors from the given input-output data is developed. A certainty factor expresses the degree to which a fuzzy if-then rule is fitting to the given data. Fuzzy if-then rules with certainty factors are generated without optimization techniques. The obtained fuzzy if-then rules can be regarded as an approximator of a non-linear function. This method is applied to GMDH (Group Method of Data Handling) to cope with difficulty in approximating multi-input functions with fuzzy if-then rules.

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Nonlinear Characteristics of Non-Fuzzy Inference Systems Based on HCM Clustering Algorithm (HCM 클러스터링 알고리즘 기반 비퍼지 추론 시스템의 비선형 특성)

  • Park, Keon-Jun;Lee, Dong-Yoon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.13 no.11
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    • pp.5379-5388
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    • 2012
  • In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.