• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.31-38
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• 1988

This paper presents a methodology for the development of a part classification and coding system suited to each individual company. When coding a group of parts for a specific company by a general purpose part classification & coding system like OPITZ system, it is frequently observed that we use only a small subset of total available code numbers. Such sparsity in the actual occurrences of code numbers implies that we can design a better system which uses digits of the system more parsimoniously. A 2-dimensional fuzzy ISODATA algorithm is developed to extract the important characteristics for the classification from the set of given parts. Based on the extracted characteristics nd the distances between fuzzy clustering cenetroids, a company-unique classification and coding system can be developed. An example case study for a medium sized machine shop is presented.

• Journal of information and communication convergence engineering
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• v.10 no.2
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• pp.149-155
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• 2012

This paper presents mobile robot control architecture of hierarchical behaviors, inspired by biological life. The system is reactive, highly parallel, and does not rely on representation of the environment. The behaviors of the system are designed hierarchically from the bottom-up with priority given to primitive behaviors to ensure the survivability of the robot and provide robustness to failures in higher-level behaviors. Fuzzy logic is used to perform command fusion on each behavior's output. Simulations of the proposed methodology are shown and discussed. The simulation results indicate that complex tasks can be performed by a combination of a few simple behaviors and a set of fuzzy inference rules.

• Proceedings of the KSR Conference
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• 2006.11a
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• pp.145-159
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• 2006

This paper describes the development of a clustering based fuzzy controller of an electromagnetic suspension vehicle using gain scheduling method and Kalman filter for a simplified single magnet system. Electromagnetic suspension vehicle systems are highly nonlinear and essentially unstable systems For achieving the levitation control of the DC electromagnetic suspension system, we considered a fuzzy system modeling method based on clustering algorithm which a set of input/output data is collected from the well defined Linear Quadratic Gaussian(LQG) controller. Simulation results show that the proposed clustering based fuzzy controller methodology robustly yields uniform performance with adequate gap response over the mass variation range.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.138-138
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and a fuzzy logic controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system is characterized by a nonlinear equation with coulomb friction. The fuzzy logic controller regulates the error between the set-point and the system output which may be caused by disturbances and it simultaneously traces the change o( the reference input. The parameters of the model are estimated by a genetic a]gorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2408-2410
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• 2001

This paper presents the control of an underactuated two-link robot called the Pendubot. Combining linearized state feedback control with Takagi-Sugeno(T-S) fuzzy controller wide-range stabilization of Pendulum is achieved. The local stabilization controler is designed by linearinzing the dynamic equations about the several desired set point and using LQR(Linear Quadratic Regulator) techniques. Takagi-Sugeno methodology is used to control the nonlinear models near different operation points. Fuzzy controller is obtained by the fuzzy blending of the local controllers. The paper includes a description of the algorithm as well as real time experimental results for the Pendubot.

• Journal of Korea Society of Industrial Information Systems
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• v.2 no.2
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• pp.159-179
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• 1997

Traditionally, the evaluation and selection of suppliers have been a major purchasing function. A growing concern for just in-time purchasing, global sourcing, and long-term partnership between buyers and suppliers makes selecting a righ supplier become more critical decision making process. Consequently, a rigorous and systematic method for evaluation suppliers is a must. However, assessing the values of factors(e.g. qulaity , delivery, and service) selected for evaluating suppliers contains elements of uncertainty. Although several methods have been developed for uncertainty analysis, they may not be proper tools for evaluating suppliers under uncertainty. In this paper, a methodology using a fuzzy-set approach in combination with a multicriterion decision-making (MCDM) technique is developed to use as a tool for evaluating suppliers under uncertainty. An numerical example is presented to demonstrate the method in practice.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.44-49
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• 2001

This paper is concerned with development of a multi-attribute structured decision model. In this study, we used AHP(analytic hierarchy process) and fuzzy set ranking methodology to overcome the multi-attributes structured decision problems ; such as multi-objective, multi-criterion, and multi-attributes. We proposed a 2-step approach : 1) individual evaluation and 2) integration of individual evaluations. In the first step, we define the performance factors and construct ana]isis structure, and in the second step performance evaluation by individual evaluators, and in second step, the results of individual evaluations are integrate. Also we developed a systematic and practical computer program to solve the problems according to the proposed methods. The proposed approach was known to be effective through a set of sample problems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.2
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• pp.391-398
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• 2009

In this study, we introduce the design methodology of an optimized fuzzy cascade controller with the aid of hierarchical fair competition-based genetic algorithm(HFCGA) for ball & beam system. The ball & beam system consists of servo motor, beam and ball, and remains mutually connected in line in itself. The ball & beam system determines the position of ball through the control of a servo motor. The displacement change the position of ball leads to the change of the angle of the beam which determines the position angle of a servo motor. Consequently the displacement change of the position of the moving ball and its ensuing change of the angle of the beam results in the change of the position angle of a servo motor. We introduce the fuzzy cascade controller scheme which consists of the outer(1st) controller and the inner(2nd) controller as two cascaded fuzzy controllers, and auto-tune the control parameters(scaling factors) of each fuzzy controller using HFCGA. The inner controller controls the position of lever arm which corresponds to the position angle of a servo motor and the outer controller decides the set-point value of the inner controller. HFCGA is a kind of parallel genetic algorithms(PGAs), and helps alleviate the premature convergence being generated in conventional genetic algorithms (GAs). For a detailed comparative analysis from the viewpoint of the performance results and the design methodology, the proposed method for the ball & beam system which is realized by the fuzzy cascade controller based on HFCGA, is presented in comparison with the conventional PD cascade controller based on serial genetic algorithms.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.70-77
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• 2002

This paper describes a design driven approach to drive intelligent test plan generation based on adaptive use case (3,5). Its foundation is an object-oriented software design approach which partitions design schema into design architecture of functional components called “design component”. A use case software development methodology of adaptive use case approach developed in I.I .T is employed which preserves this unit architecture on through to the actual code structure. Based on the partition design schema produced during the design phase of this methodology, a test plan is generated which includes a set of component and scenario based test. A software metric is introduced which produces an ordering of this set to enhance productivity and both promote and capitalize on test case reusability, This paper contains an application that illustrates the proposed approach.