• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.516-525
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• 2005

Case-based reasoning (CBR) has been widely used in various areas due to its convenience and strength in complex problem solving. Generally, in order to obtain successful results from CBR, effective retrieval of useful prior cases for the given problem is essential. However, designing a good matching and retrieval mechanism for CBR systems is still a controversial research issue. Most prior studies have tried to optimize the weights of the features or selection process of appropriate instances. But, these approaches have been performed independently until now. Simultaneous optimization of these components may lead to better performance than in naive models. In particular, there have been few attempts to simultaneously optimize the weight of the features and selection of the instances for CBR. Here we suggest a simultaneous optimization model of these components using a genetic algorithm (GA). We apply it to a customer classification model which utilizes demographic characteristics of customers as inputs to predict their buying behavior for a specific product. Experimental results show that simultaneously optimized CBR may improve the classification accuracy and outperform various optimized models of CBR as well as other classification models including logistic regression, multiple discriminant analysis, artificial neural networks and support vector machines.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.10
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• pp.6276-6281
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• 2014

Genetic Algorithms (GAs) are efficient methods for search and optimization problems. On the other hand, there are some problems associated with the premature convergence to local optima of the multimodal function, which has multi peaks. The problem is related to the lack of genetic diversity of the population to cover the search spaces sufficiently. A sharing and crowding method were introduced. This paper proposed strategies to improve the convergence speed and the convergence to the global optimum for solving the multimodal optimization function. These strategies included the random generated sub-population that were well-distributed and spread widely through search spaces. The results of the simulation verified the effects of the proposed method.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.524-526
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• 1999

In the construction of successful fuzzy models and/or controllers for nonlinear systems, the identification of a good fuzzy inference system is an important yet difficult problem, which is traditionally accomplished by a time-consuming trial-and-error process. In this paper, we propose a systematic identification procedure for complex multi-input single-output nonlinear systems with DNA coding method. A DNA coding method is optimization algorithm based on biological DNA as conventional genetic algorithms(GAs) are. The strings in the DNA coding method are variable-length strings, while standard GAs work with a fixed-length coding scheme. the DNA coding method is well suited to learning because it allows a flexible representation of a fuzzy inference system. We also propose a new coding method fur applying the DNA coding method to the identification of fuzzy models. This coding scheme can effectively represent the zero-order Takagi-Sugeno(TS) fuzzy model. To acquire optimal TS fuzzy model with higher accuracy and economical size, we use the DNA coding method to optimize the parameters and the number of fuzzy inference system. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a Duffing-forced oscillation system.

• Journal of Korea Game Society
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• v.2 no.2
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• pp.60-65
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• 2002

In the majority of today's computer games, the behaviour of characters are controlled by pre-defined game logic or pre-generated motion. As game developers strive for richer and more interactive games, they often encounter limitations with this approach. This paper attempts to construct a game model using Genetic Algorithms (GAs) in order to produce more intelligent and compelling computer games. Based on teaming ability, the use of GAs will enable the characters to continually evolve, providing a changing and dynamic game environment. A real-time game was implemented to investigate the performance and limitations of the system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.374-377
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• 2006

본 논문은 에어컨 시스템의 효율성과 안정성에 기초하여, 과열도와 저압을 제어하는 Fuzzy 제어기 설계를 제안한다. 에어컨 시스템은 Compressor(압축기), Condenser(응축기), Evaporator(증발기), Expansion Valve(확장 밸브) 로 구성되며, 각각의 기기에 대한 제어가 독립적으로 이루어져 있다. 기존의 제어가 한 제어기를 사용한 단일방식으로 이루어지다보니 에어컨 시스템의 특성인 냉매의 상태가 달라지면 시스템 전반적으로 그 영향이 파급되는 부분까지 고려를 해 주지 못하고, 제어기의 성능이 효율적이고 안정적이지 못했다. 본 논문에서는 에어컨 시스템의 효율과 안정도에 결정적인 영향을 미치는 과열도와 저압(증발기의 압력)을 제어하기 위해, 비선형성이 강하고 불확실하며 복잡한 시스템을 쉽게 제어할 수 있는 Fuzzy 제어기를 구성하여, Expansion Valve 와 Compressor 에서 동시에 제어하는 Multi 제어기를 설계한다. 제안된 Fuzzy 제어기는 이산형 lookup_table 방식과 연속형 간략추론 방식을 사용하여 제어기를 설계하고, 유전자 알고리즘(GAs)을 이용하여 최적의 Fuzzy 제어기의 환산계수를 구한다. 그리고 시뮬레이션 결과를 통해 이산형 lookup_table 방식과 연속형 간략추론 방식의 각각의 제어기를 사용한 결과를 비교한다.

• International Journal of Control, Automation, and Systems
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• v.3 no.2
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• pp.183-194
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• 2005

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the premise part of the rule-based structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. We distinguish between two types of the linear fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, the models are experimented with a representative numerical example. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when comparing with other neurofuzzy models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.161-164
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• 2007

우리는 알지 못하는 장소를 찾기 위해 네비게이션을 이용한다. 아직은 단순히 최단거리를 알려주는 네비게이션이 주를 이루고 있다. 하지만 그 길이 최적의 경로가 되는 것은 아니다. 이 논문에서는 이러한 점을 보완하기 위한 새로운 방법을 제시하고 있다. RFID 리더기와 카드를 이용하여 이용자의 출입을 체크함으로써 실시간으로 변하는 각 장소의 인원현황을 알 수 있다. 그리고 거리, 혼잡도, 선호도 등의 몇 가지 정보들을 토대로 퍼지와 유전자 알고리즘을 기반으로 하는 TSP를 이용하여 각각의 이용자 성향에 맞는 최적의 경로를 알 수 있다. 접근성을 높이기 위해 최적의 경로를 보여주는 디스플레이를 장착한 로봇을 이용한다. 다양한 컨텐츠를 포함시키면 더욱 발전한 안내 시스템으로서의 구현이 가능하다.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.217-219
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• 2006

본 연구는 정보입자와 유전알고리즘의 기호코딩을 통해 퍼지집합 기반 다항식 뉴럴네트워크(IG based gFSPNN)의 최적 설계 제안한다. 기존의 Furry Srt-based Polynomial Neural Networks의 최적설계를 위해 유전자 알고리즘의 이진코딩을 사용하였다. 이지코딩은 스티링 길이 때문에 연산시간이 급격히 증가되는 현상과 해밍절벽(Hamming Cliff)에 따른 급격한 비트변환이 힘들다는 단점이 내제 하였다. 이에 본 논문에서는 스티링 길이와 해밍절벽에 따른 문제를 해결 하기위해 기호코딩을 사용하였다._데이터들의 특성을 모델에 반영하기 위해 Hard C-Means(HCM)을 결합한 Information Granulation(IG)을 사용하여 최적모델 구축 속도를 빠르게 하였다. 실험적 예제를 통하여 제안된 모델의 성능을 평가한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.438-441
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• 2004

In this study, we introduce a noble neurogenetic approach to the design of fuzzy controller. The design procedure dwells on the use of Computational Intelligence (CI), namely genetic algorithms and Fuzzy-Set based Neural Networks (FSNN). The crux of the design methodology is based on the selection and determination of optimal values of the scaling factors of the fuzzy controllers, which are essential to the entire optimization process. First, the tuning of the scaling factors of the fuzzy controller is carried out by using GAs, and then the development of a nonlinear mapping for the scaling factors is realized by using GA based FSNN. The developed approach is applied to a nonlinear system such as an inverted pendulum where we show the results of comprehensive numerical studies and carry out a detailed comparative analysis.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.416-419
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• 2001

In this paper a new approach to estimate scaling factors of fuzzy controllers such as the fuzzy PID controller and the fuzzy PD controller is presented. The performance of the fuzzy controller is sensitive to the variety of scaling factors[1]. The desist procedure dwells on the use of evolutionary computing(a genetic algorithm) and estimation algorithm for dynamic systems (the inverted pendulum). The tuning of the scaling factors of the fuzzy controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy controller by means of two types of estimation algorithms such as Neuro-Fuzzy model, and regression polynomial [7]. This method can be applied to the nonlinear system as the inverted pendulum. Numerical studies are presented and a detailed comparative analysis is also included.