• Title, Summary, Keyword: Successive Tuning Method

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Optimization of Fuzzy Set-Fuzzy Systems based on IG by Means of GAs with Successive Tuning Method

  • Park, Keon-Jun;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Journal of Electrical Engineering and Technology
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    • v.3 no.1
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    • pp.101-107
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    • 2008
  • We introduce an optimization of fuzzy set-fuzzy systems based on IG (Information Granules). The proposed fuzzy model implements system structure and parameter identification by means of IG and GAs. The concept of information granulation was coped with to enhance the abilities of structural optimization of the fuzzy model. Granulation of information realized with C-Means clustering helps determine the initial parameters of the fuzzy model such as the initial apexes of the membership functions in the premise part and the initial values of polynomial functions in the consequence part of the fuzzy rules. The initial parameters are adjusted effectively with the help of the GAs and the standard least square method. To optimally identify the structure and the parameters of the fuzzy model we exploit GAs with successive tuning method to simultaneously search the structure and the parameters within one individual. We also consider the variant generation-based evolution to adjust the rate of identification of the structure and the parameters in successive tuning method. The proposed model is evaluated with the performance of the conventional fuzzy model.

Genetic Optimization of Fyzzy Set-Fuzzy Model Using Successive Tuning Method (연속 동조 방법을 이용한 퍼지 집합 퍼지 모델의 유전자적 최적화)

  • Park, Keon-Jun;Oh, Sung-Kwun;Kim, Hyun-Ki
    • Proceedings of the KIEE Conference
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    • pp.207-209
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    • 2007
  • In this paper, we introduce a genetic optimization of fuzzy set-fuzzy model using successive tuning method to carry out the model identification of complex and nonlinear systems. To identity we use genetic alrogithrt1 (GA) sand C-Means clustering. GA is used for determination the number of input, the seleced input variables, the number of membership function, and the conclusion inference type. Information Granules (IG) with the aid of C-Means clustering algorithm help determine the initial paramters of fuzzy model such as the initial apexes of the, membership functions in the premise part and the initial values of polyminial functions in the consequence part of the fuzzy rules. The overall design arises as a hybrid structural and parametric optimization. Genetic algorithms and C-Means clustering are used to generate the structurally as well as parametrically optimized fuzzy model. To identify the structure and estimate parameters of the fuzzy model we introduce the successive tuning method with variant generation-based evolution by means of GA. Numerical example is included to evaluate the performance of the proposed model.

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A Study on Successive Approximation Measurement of Mechanical Parameters for Motor Control System (축차비교를 이용한 전동기 기계정수 추정에 관한 연구)

  • Ahn, Jong-Keon;Paek, Yoon-Hyeok;Park, Seung-Kyu;Ahn, Ho-Gyun
    • Proceedings of the KIEE Conference
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    • pp.165-167
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    • 2001
  • This paper presents a study on successive approximation measurement of mechanical parameters for motor control system. At the first step of servo system installation, control system gain tuning is troublesome work. Recently, auto-tuning method of motion controller for motor drive system is based on parameter measurement and identification. On the case of first order mechanical system (mechanical parameters are modified by simple inertia and friction), it is necessary for good response to get the accurate measurement or identification of the mechanical parameters. In this paper, novel method applies the binary successive approximation measurement to the inertia and friction coefficient. Computer simulation and experiment for the proposed method will show verification of accuracy and usefulness.

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A Study on Successive Approximation Measurement of Mechanical Parameters for Motor Control System (전동기 제어시스템 기계정수의 점근적 추정에 관한 연구)

  • Ahn Jong-Keon;Park Seung-Kyu;Ahn Ho-Gyun
    • Proceedings of the KIPE Conference
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    • pp.138-141
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    • 2001
  • This paper presents a study on successive approximation measurement of mechanical parameters for motor control system. At the first step of servo system installation, control system gain tuning is troublesome work. Recently, autotuning method of motion controller for motor drive system is based on parameter measurement and identification. On the case of first order mechanical system (mechanical parameters are modified by simple inertia and friction), it is necessary for good response to get the accurate measurement or identification of the mechanical parameters . In this paper, novel method applies the binary successive approximation measurement to the inertia and friction coefficient. Computer simulation and experiment for the proposed method will show verification of accuracy and usefulness.

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Design of Type-2 FCM-based Fuzzy Inference Systems and Its Optimization (Type-2 FCM 기반 퍼지 추론 시스템의 설계 및 최적화)

  • Park, Keon-Jun;Kim, Yong-Kab;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.11
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    • pp.2157-2164
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    • 2011
  • In this paper, we introduce a new category of fuzzy inference system based on Type-2 fuzzy c-means clustering algorithm (T2FCM-based FIS). The premise part of the rules of the proposed model is realized with the aid of the scatter partition of input space generated by Type-2 FCM clustering algorithm. The number of the partition of input space is composed of the number of clusters and the individual partitioned spaces describe the fuzzy rules. Due to these characteristics, we can alleviate the problem of the curse of dimensionality. The consequence part of the rule is represented by polynomial functions with interval sets. To determine the structure and estimate the values of the parameters of Type-2 FCM-based FIS we consider the successive tuning method with generation-based evolution by means of real-coded genetic algorithms. The proposed model is evaluated with the use of numerical experimentation.

Optimization and Verification of Parameters Used in Successive Zooming Genetic Algorithm (순차적 주밍 유전자 알고리즘 기법에 사용되는 파라미터의 최적화 및 검증)

  • KWON YOUNG-DOO;KWON HYUN-WOOK;KIM JAE-YONG;JIN SEUNG-BO
    • Journal of Ocean Engineering and Technology
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    • v.18 no.5
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    • pp.29-35
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    • 2004
  • A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.

Structural damage detection in continuum structures using successive zooming genetic algorithm

  • Kwon, Young-Doo;Kwon, Hyun-Wook;Kim, Whajung;Yeo, Sim-Dong
    • Structural Engineering and Mechanics
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    • v.30 no.2
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    • pp.135-146
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    • 2008
  • This study utilizes the fine-tuning and small-digit characteristics of the successive zooming genetic algorithm (SZGA) to propose a method of structural damage detection in a continuum structure, where the differences in the natural frequencies of a structure obtained by experiment and FEM are compared and minimized using an assumed location and extent of structural damage. The final methodology applied to the structural damage detection is a kind of pseudo-discrete-variable-algorithm that counts the soundness variables as one (perfectly sound) if they are above a certain standard, such as 0.99. This methodology is based on the fact that most well-designed structures exhibit failures at some critical point due to manufacturing error, while the remaining region is free of damage. Thus, damage of 1% (depending on the given standard) or less can be neglected, and the search concentrated on finding more serious failures. It is shown that the proposed method can find out the exact structural damage of the monitored structure and reduce the time and amount of computation.

Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems (연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬)

  • Gwon, Yeong-Du;Gwon, Sun-Beom;Gu, Nam-Seo;Jin, Seung-Bo
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.26 no.2
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    • pp.406-414
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    • 2002
  • A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

Identification of Fuzzy System Driven to Parallel Genetic Algorithm (병렬유전자 알고리즘을 기반으로한 퍼지 시스템의 동정)

  • Choi, Jeoung-Nae;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
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    • pp.201-203
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    • 2007
  • The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

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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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    • 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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