• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1808-1811
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• 1997

A position control algorithm for a flexible manipulato is stuudied. The proposed algorithm is based on a fuzzy theroy with a Steady State Genetic Algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is one of the optimization algorithms, tunes automatically the input-output membership parameters and fuzzy rules. The computer simulation is presented ot illustrate the approaches. Finally we applied a fuzzy theory with a SSGA to aposition control of a flexible manipulator.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.485-493
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• 2005

Using 1991 FDIC financial statement data, we develop fuzzy clusters of the data set. We also identify the distinctive characteristics of the fuzzy clustering algorithm and compare the closest hard-partitioning result of the fuzzy clustering algorithm with the outcomes of two self-organizing neural networks. When nine clusters are used, our analysis shows that the fuzzy clustering method distinctly groups failed and extreme performance banks from control (healthy) banks. The experimental results also show that the fuzzy clustering method and the self-organizing neural networks are promising tools in identifying potentially failing banks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.648-650
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• 1998

This paper proposes a constructive algorithm for generating the Takagi-Sugeno type fuzzy model through the sequential learning from training data set. The proposed algorithm has a two-stage learning scheme that performs both structure and parameter learning simultaneously. The structure learning constructs fuzzy model using two growth criteria to assign new fuzzy rules for given observation data. The parameter learning adjusts the parameters of existing fuzzy rules using the LMS rule. To evaluate the performance of the proposed fuzzy modeling approach, well-known benchmark is used in simulation and compares it with other modeling approaches.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2000년도 춘계학술발표대회
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• pp.177-180
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• 2000

An algorithm for modeling of yeast fermentation process using genetic-fuzzy algorithm is presented in this work. The algorithm involves developing the fuzzy modeling of the process and model update capability against the system change. The membership functions of state variables and specific rates and the decision table were generated using genetic algorithm. This algorithm could replace the complex mathematical model to simple fuzzy model and cope with the change of process characteristics well.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.474-478
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• 1998

This paper proposes a method which can resolve the problem of exisiting fuzzy PI controller using optimal scaling gains obtained by genetic algorithm. The new method adapt a fuzzy logic controller as a high level controller to perform scaling gain algorithm between two pre-determined sets.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.622-624
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• 2005

This paper proposes a time series prediction method for the nonllinear system using the fuzzy system and its genetic algorithm, At first, we obtain the optimal fuzzy membership function using the genetic algorithm. With the optimal fuzzy rules and its input differences, a better time prediction series system may be obtained. We obtain a good result for the time prediction of the electric load.

• 한국경영과학회지
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• 제18권2호
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• pp.23-43
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• 1993

This paper employs fuzzy variables instead of deterministic variables for job times in a project network. A fuzzy variable has its value restricted by a possibility distribution. This paper utilizes the triangular possibility distribution which has three estimated times. That is normal, resonable, and crash job times. This paper develops a fuzzy k-longest path algorithm, by utilizing the k-longest path algorithm. This algorithm will be useful to control the project the project network by considering the project completion possibility.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.373-373
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• 2000

In this paper, we propose a fuzzy modeling algorithm which divides the input space more efficiently than convention methods by taking into consideration correlations between components of sample data. The proposed fuzzy modeling algorithm consists of two steps: coarse tuning, which determines consequent parameters approximately using FCRM clustering method, and fine tuning, which adjusts the premise and consequent parameters more precisely by gradient descent algorithm. To evaluate the performance of the proposed fuzzy mode, we use the numerical data of nonlinear function.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.645-647
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• 1998

A position control algorithm for a inverted pendulum is studied. The proposed algorithm is based on a fuzzy theory and a steady state genetic algorithm(SSGA). The conventional fuzzy methods need expert's knowledges or human experiences. The SSGA, which is a optimization algorithm, tunes the input-output membership parameters and fuzzy rules automatically. The computer simulation to control a inverted pendulum is presented to illustrate the approaches.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1246-1249
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• 1993

In this paper, we propose an algorithm of obstacle avoidance using fuzzy inferences. After the basic idea of the path generation algorithm using piecewise polynomials is described, the obstacle avoidance problem using fuzzy inferences is considered. Main concept of the avoidance algorithm is to modify intermittent point data using fuzzy inferences and to generate the collision free path based on the modified data. Finally, simulation result demonstrate the effectiveness of the proposed algorithm.