• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.199-202
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• 2000

본 논문에서는 퍼지-유전자 접근방법을 이용한 무감독 특징 선택방법에 대하여 나타내었다. 이 방법은 각각의 특징들의 중요도에 따라 순서를 정하기 위해 사용되는 weighted distance 를 포함하는 특징 평가 지표 (feature evaluation index)를 최소화시키는데 있다. 또한 특징 평가 지표에서 사용되는 각 패턴들의 쌍에 대하여 근접함의 정도를 퍼지 멤버쉽 함수를 이용하여 결정하고 유전자 알고리즘은 평가 지표를 최소화시킴으로써 각 특징의 중요도를 나타내는 최적의 weighting 계수의 집합을 한기 위하여 적용하였다.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2953-2955
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• 2000

본 논문에서는 전문가의 지식이 없는 상황에서 자동적으로 최적의 퍼지 제어기를 설계하는 방법에 대해 연구한다. 먼저 퍼지 제어기의 규칙 설정을 위해 기존의 PID 제어기의 입출력 데이터를 클러스터링한다. 군집된 데이터들로부터 클러스터의 수를 파악하고 이를 바탕으로 퍼지 제어를 위한 규칙의 수를 결정한다. 둘째로 퍼지 제어기의 여러 파라미터들은 유전자 알고리즘을 적용하여 최적화한다. GA를 이용한 최적화 과정에서는 성능평가 기준으로 기준입력에 대한 시스템 응답간의 오차와 오버슈트의 크기를 사용하여 응답이 빠르고 안정적인 제어기를 설계하도록 진화방향을 설정한다. 이렇게 만들어진 퍼지 제어기의 성능을 기존의 PID 제어기와 비교 평가한다

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2696-2698
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• 2000

본 연구에서는 실수형 염색체들로 구성된 개체에 대해 감수분열을 적용하여 개체를 만들고, 이 생식체들의 랜덤한 선택과 교배에 의해 세대가 진화함에 따라 탐색을 수행하는 감수분열 유전알고리즘을 이용하여 퍼지모델의 최적 구조와 파라미터를 탐색하고 Gradient Descent 알고리즘으로 파라미터를 정밀 조정하는 방안을 제안한다. 제안된 방안을 적용하여 Box-Jenkins의 가스로 데이터에 대한 퍼지모델을 구성하고 그 적용 가능성을 보인다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.203-210
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• 1995

본 논문에서는 주어진 입출력 데이터로부터 유전자 알고리즘을 이용하여 퍼지제어 기를 자동 생성하는 방법에 대하여 기술한다. 주어진 입출력 데이터를 표현하는 퍼지제어기 는 각 유전자에 암호화되고, 퍼지제어기를 표현하는 각 유전자들은 서로 정보를 교환함으로 써 주어진 데이터를 적절히 표현하는 퍼지제어기를 탐색하게 된다. 유전자는 각 입력 변수 의 언어항을 정의하고, 퍼지제어규칙은 정의된 언어항과 주어진 데이터로부터 생성된다. 탐 색과정에서 퍼지제어기의 제어규칙과 각 입력변수의 언어항의 개수와 위치는 계속 변화하여 주어진 입출력 데이터를 잘 설명하는 퍼지제어기를 찾는다.

• Journal of Navigation and Port Research
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• v.31 no.8
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• pp.697-703
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• 2007

In this paper, we focuses on the development of Takagi-Sugeno (T-S) fuzzy modeling in a nonlinear container crane system. A T-S fuzzy model is characterized by fuzzy "if-then" rules which represent the locally input-output relationship whose consequence part is described by a state space equation as subsystem. The T-S fuzzy model in container cranes first obtains a few number of linear models according to operation conditions and blends these conditions using fuzzy membership functions. Parameters of the membership functions are adjusted by a RCGA to have same dynamic characteristics with nonlinear system of a container crane. Simulations are given to illustrate the performance of T-S fuzzy model.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.7
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• pp.378-388
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• 2000

In this paper, we propose Fuzzy Polynomial Neural Networks(FPNN) based on Polynomial Neural Networks(PNN) and Fuzzy Neural Networks(FNN) for model identification of complex and nonlinear systems. The proposed FPNN is generated from the mutually combined structure of both FNN and PNN. The one and the other are considered as the premise part and consequence part of FPNN structure respectively. As the consequence part of FPNN, PNN is based on Group Method of Data Handling(GMDH) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and self-organizing networks that can be generated. FPNN is available effectively for multi-input variables and high-order polynomial according to the combination of FNN with PNN. Accordingly it is possible to consider the nonlinearity characteristics of process and to get better output performance with superb predictive ability. As the premise part of FPNN, FNN uses both the simplified fuzzy inference as fuzzy inference method and error back-propagation algorithm as learning rule. The parameters such as parameters of membership functions, learning rates and momentum coefficients are adjusted using genetic algorithms. And we use two kinds of FNN structure according to the division method of fuzzy space of input variables. One is basic FNN structure and uses fuzzy input space divided by each separated input variable, the other is modified FNN structure and uses fuzzy input space divided by mutually combined input variables. In order to evaluate the performance of proposed models, we use the nonlinear function and traffic route choice process. The results show that the proposed FPNN can produce the model with higher accuracy and more robustness than any other method presented previously. And also performance index related to the approximation and prediction capabilities of model is evaluated and discussed.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.43-58
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• 2014

As the use of trading systems has increased rapidly, many researchers have become interested in developing effective stock market prediction models using artificial intelligence techniques. Stock market prediction involves multifaceted interactions between market-controlling factors and unknown random processes. A successful stock prediction model achieves the most accurate result from minimum input data with the least complex model. In this research, we develop a combination model of ${\pi}$-fuzzy logic and support vector machine (SVM) models, using a genetic algorithm to optimize the parameters of the SVM and ${\pi}$-fuzzy functions, as well as feature subset selection to improve the performance of stock market prediction. To evaluate the performance of our proposed model, we compare the performance of our model to other comparative models, including the logistic regression, multiple discriminant analysis, classification and regression tree, artificial neural network, SVM, and fuzzy SVM models, with the same data. The results show that our model outperforms all other comparative models in prediction accuracy as well as return on investment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.894-897
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• 1995

This paper focuses on self-organization of fuzzy rules for performance management of computer communication networks serving manufacturing systems. The performance management aims to improve the network performance in handling various types of messages by on-line adjustment of protocol parameters. The principle of fuzzy logic has been used in representing the knowledge of human expert on the performance management and in deriving management decisions. In this paper, we present applications of genetic algorithm, simulated annealing, and evolution strategies to find a better set of rules for various network conditions. The efficacy of this self-organization is demonstrated by discrete simulation of an IEEE 802.4 network.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.797-799
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• 1999

In this paper, we design the Multi-FNN(Fuzzy-Neural Networks) using HCM Method. The proposed Multi-FNN uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. Also, We use HCM(Hard C-Means) method of clustering technique for improvement of output performance from pre-processing of input data. The parameters such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. We use the training and testing data set to obtain a balance between the approximation and the generalization of our model. Several numerical examples are used to evaluate the performance of the our model. From the results, we can obtain higher accuracy and feasibility than any other works presented previously.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1160-1168
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• 2000

Due to the increase in importance of factory-automation (FA) in the field of production, the importance of he autonomous guided vehicle's (AGV) role has also increased. This paper is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an behavior-based system evolving by itself is also being considered. In this paper, constructed an active and effective AGV fuzzy controller to be able to carry out self-organization. To construct it, we tuned suboptimally membership function using a genetic algorithm (GA) and improved the control efficiency by self-correction and the generation of control rules.