• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.581-584
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• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1996년도 추계학술대회 학술발표 논문집
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• pp.162-165
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• 1996

In this paper, we proposed an optimization method of the membership function and the numbers of fuzzy rule base for the stabilization controller of the inverted pendulum system by genetic algorithm(GAs). Conventional methods to these problems need to an expert knowledge or human experience. The proposed genetic algorithm method will tune automatically the input-output membership parameters and will optimize their rule-base.

• 지능정보연구
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• 제3권2호
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• pp.11-22
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• 1997

본 연구에서는 비선형 최적화 문제를 효율적으로 해결하기 위한 혼합유전알고리즘(Hybrid Genetic Algorthm : HGA)을 개발하였다. HGA는 기존 유전알고리즘의 적용에 있어 문제점으로 지적된 정밀도의 적용문제와 벌금함수의 사용을 배제하였으며 지역적최적점으로 빠르게 수렴하는 기존의 지역적 탐색법과 유전알고리즘 적용이후 수렴된 해 주변에 대한 정밀탐색법을 함께 고려하여 설계하였으며 이를 세가지의 비선형 최적화 문제 적용하여 본 논문에서 개발한 HGA의 유효성을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.820-822
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• 1996

This paper proposes a method for seeking the scheme of substation restoration by using genetic algorithm. Genetic algorithm (GA), first introduced by John Holland, is becoming an important tool in machine learning and function optimization. GA is a searching or optimization algorithm based on Darwinian biological evolution principle. As a test system, we assume a simple substation system and for the transformer fault, the result is obtained.

• 산업공학
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• 제25권3호
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• pp.319-326
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• 2012

To effectively predict financial crisis, this paper presents an early warning system based on artificial intelligence technologies. Both Genetic Algorithms and Neural Networks are utilized for the proposed system. First, a genetic algorithm has been developed for the effective selection of economic indices, which are used for monitoring financial crisis. Then, an optimum weight of the selected indices has been determined by a neural network method. To validate the effectiveness of the proposed system, a series of experiments has been conducted by using the Korean economic indices from 2005 to 2008.

• 산업공학
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• 제21권3호
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• pp.254-261
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• 2008

Connected-(r,s)-out of-(m,n) : F system is an important topic in redundancy design of the complex system reliability and it's maintenance policy. Previous studies applied Monte Carlo simulation and genetic, simulated annealing algorithms to tackle the difficulty of maintenance policy problem. These algorithms suggested most suitable maintenance cycle to optimize maintenance pattern of connected-(r,s)-out of-(m,n) : F system. However, genetic algorithm is required long execution time relatively and simulated annealing has improved computational time but rather poor solutions. In this paper, we propose the ant colony optimization approach for connected-(r,s)-out of-(m,n) : F system that determines maintenance cycle and minimum unit cost. Computational results prove that ant colony optimization algorithm is superior to genetic algorithm, simulated annealing and tabu search in both execution time and quality of solution.

• 대한전기학회논문지:전력기술부문A
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• 제50권2호
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• pp.58-66
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• 2001

This paper presents an application of a newly designed Adaptive Genetic Algorithm (AGA) to solve the Optimal Feeder Routing (OFR) problem for distribution system planning. The main objective of the OFR problem usually is to minimize the total cost that is the sum of investment costs and system operation costs. We propose a properly designed AGA, in this paper, which can handle the horizon-year expansion planning problem of power distribution network in which the location of substation candidates, the location and amount of forecasted demands are given. In the proposed AGA, we applied adaptive operators using specially designed adaptive probabilities. we also a Simplified Load Flow (SLF) technique for radial networks to improve a searching efficiency of AGA. The proposed algorithm has been evaluated with the practical 32, 69 bus test system to show favorable performance. It is also shown that the proposed method for the OFR can also be used for the network reconfiguration problem in distribution system.

• 한국지능시스템학회논문지
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• 제16권2호
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• pp.228-233
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• 2006

This paper introduces design method for air pump system using bond graph and genetic programming to maximize outflow subject to a constraint specifying maximum power consumption. The air pump system is a mixed domain system which includes electromagnetic, mechanical and pneumatic elements. Therefore an appropriate approach for a better system for synthesis is required. Bond graphs are domain independent, allow free composition, and are efficient for classification and analysis of models. Genetic programming is well recognized as a powerful tool for open-ended search. The combination of these two powerful methods, BG/GP, was tested for redesign of air pump system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.15-17
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• 2005

Recently genetic algorithm techniques have widely used in adaptive and control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of genetic algorithm constructed as a result is not obvious. And this method has been used as a learning algorithm to estimate the parameter of a genetic algorithm used for identification of the process dynamics of nonlinear IIR filter and it was shown that this method offered superior capability over the genetic algorithm. A genetic algorithm is used to solve the parameter identification problem for linear and nonlinear digital filters. This paper goal estimate nonlinear IIR filter parameter using the genetic algorithm.