• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.708-711
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• 1997

This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

• Journal of the Korean Operations Research and Management Science Society
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• v.33 no.2
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• pp.1-16
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• 2008

The purpose of this paper is to propose a new binary classification method for predicting corporate failure based on genetic algorithm, and to validate its prediction power through empirical analysis. Establishing virtual companies representing bankrupt companies and non-bankrupt ones respectively, the proposed method measures the similarity between the virtual companies and the subject for prediction, and classifies the subject into either bankrupt or non-bankrupt one. The values of the classification variables of the virtual companies and the weights of the variables are determined by the proper model to maximize the hit ratio of training data set using genetic algorithm. In order to test the validity of the proposed method, we compare its prediction accuracy with ones of other existing methods such as multi-discriminant analysis, logistic regression, decision tree, and artificial neural network, and it is shown that the binary classification method we propose in this paper can serve as a premising alternative to the existing methods for bankruptcy prediction.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.3
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• pp.29-41
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• 2009

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate Its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss Its methodological characteristics in comparison with other existing classification methods. Also, we conduct a series of experiments employing survey data of consumer choices of MP3 players to assess the prediction power of the model. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.54-60
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• 2001

In this paper, a shape-based block layout (SBL) approach is presented to solve the facility layout problem with unequal-area and fixed shapes. The SBL approach employs hybrid genetic algorithm (Hybrid-GA) to find a good solution and the concept of bay structure is used. In the typical facility layout problem with unequal area and fixed shapes, the given geometric constraints of unequal-area and fixed shapes are mostly approximated to original shape by aspect ratio. Thus, the layout results require extensive manual revision to create practical layouts and it produces irregular building shapes and too much unusable spaces. Experimental results show that a SBL model is able to produce better solution and to create more practical layouts than those of existing approaches.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.1-7
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• 2008

The purpose of this paper is to develop a new classification method for predicting consumer choice based on genetic algorithm, and to validate its prediction power over existing methods. To serve this purpose, we propose a hybrid model, and discuss its methodological characteristics in comparison with other existing classification methods. Also, to assess the prediction power of the model, we conduct a series of experiments employing survey data of consumer choices of MP3 players. The results show that the suggested model in this paper is statistically superior to the existing methods such as logistic regression model, artificial neural network model and decision tree model in terms of prediction accuracy. The model is also shown to have an advantage of providing several strategic information of practical use for consumer choice.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.961-965
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• 2004

본 연구에서는 상수관망의 신뢰도 기반 최적화설계에서 시스템 구성물의 기계적 고장의 영향뿐만이 아니라 관로의 수리학적 능력과 절점수요에서의 불확실성이 결합되어 인식할 수 있는 새로운 방법을 제시하였다. 수질과 연관된 신뢰도 문제는 고려하지 않았고 단지 수량의 항으로 수요자들의 요구량을 충족시키기 위해 급수관망의 공급능력을 고려하였다. 수량의 관점에서 관망의 신뢰도의 측정은 절점 수요량들이 항상 만족되어진다고 가정한 상태에서 불충분한 수두의 정도를 이용한다. 따라서 절점신뢰도는 공급되는 절점수두가 미리 규정된 최소수두를 상외하거나 충족시키는 확률로 정의되어진다. 이 모형에 의해 설계된 상수관망은 정상관망 상태구성(구성물의 고장이 발생하지 않았을 경우)와 미리 정해진 고장 시나리오의 범위와 연관된 관망의 악화된 구성상태 모두에서 절점에서의 임의의 수요량과 임의의 수리학적 능력하에서 상수공급량의 항으로 규정된 수준의 서어비스의를 제공할 수 있다. 본 모형은 다양한 관망구성에 내해 상수관망의 신뢰도의 정도를 결정하기 위해서 Monte-Carlo 모의를 이용하였다. 실제 상수관망에 내해 본 인구모형을 이용하여 신뢰도 및 최적화 해석이 수행되었다. 해석결과 본 모형은 합리적으로 관망 전체에 대해 합리적인 범위의 신뢰도를 유지하면서 관망의 건설비용의 치적화가 수행될 수 있었다.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.390-393
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• 1997

Fuzzy logic and neural networks are complemetary technologies in the design of intelligent system. Fuzzy neural network(FNN) as an auto-tuning method is actually known to an excellent method for the adjustment of the fuzzy rule. However, this has a weak point, because the convergence to the optimum depends on the initial condition. In this paper we develop a coding format to determine a FNN model by chromosome in GA and present systematic approach to identify the parameters and structure of FNN. The proposed hybrid tuning method realizes to construct minimal and optimal structure of the fuzzy mode simultaneously and automatically. This paper shows effectiveness of the tuning system by simulations compared with conventional methods.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.523-534
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• 2005

This paper is concerned with the integrated problem of line balancing and model sequencing in mixed model assembly lines(MMALBS), which is important to efficient utilization of the lines. In the problem, we deal with the objective of minimizing the overall line length To apply the GAs to MMALBS problems, we suggest a GA representation which suitable for its problems, an efficient decoding technique for the objective, and genetic operators which produce feasible offsprings. Extensive experiments are carried out to analyze the performance of the proposed algorithm. The computational results show that our algorithm is promising in solution quality.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.4
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• pp.7-15
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• 1989

A technique is suggested which enables the large signal current and voltage waveforms to be determined for a GaAs Schottky-Barrier diode mixer by extracting the algorithm for the nonlinear circuit analysis from the Gauss-Jacobi relaxation and the application of the Harmonic Balance Technique. Both the nonlinear and linear steps of the analysis are included. This analysis permitts accurate determination of the conversion loss for microwave mixer and the computer simulation provides an method applicable to MMIC design. The validity of the nonlinear and linear analysis is confirmed by comparing the simulation results with experimental data of the conversion loss.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1009-1011
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• 1996

This paper presents an approach to building multi-input and single-output fuzzy models for nonlinear data-based systems. Such a model is composed of fuzzy rules, and its output is inferred by simplified reasoning. Optimal structure and membership parameters for a fuzzy model are automatically and simultaneously identified by GA(Genetic Algorithm). Numerical examples are provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce a fuzzy model with higher accuracy and a smaller number of fuzzy rules than the ones achieved previously in other methods.