• 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 Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.297-300
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• 2006

본 논문에서는 비선형 기동표적의 추적에 대한 새로운 접근 방식을 소개한다. 이 논문에서는 표적의 가속도를 시변 변수인 표적의 추가적인 잡음으로 두고 각각의 가속도 간격의 정도에 따라 얻어지는 모든 잡음에 대한 변수에 의해 각각의 하부 모델들을 특성화시켰다. 표적의 기동중에 나타나는 가속도를 효과적으로 다루기 위하여, 잡음의 크기가 급격히 증가할 경우 증가분을 가속도로 인식하여 기동표적 관계식에 이용하였다. 또한 모르는 가속도에 따른 시변 변수를 적응적으로 어립잡기는 어렵기 때문에 정밀한 계산을 위하여 퍼지 뉴럴 네트워크와 적응 상호작용 다중모델 기법을 이용하였다. 퍼지 뉴럴 네트워크의 동정을 위해서는 오차 역전파 학습법을 사용하였다. 그리고 제안된 알고리즘의 수행 가능성을 보여주기 위하여 몇 가지 예를 제시하였다.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.145-158
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• 2016

This paper introduces a risk graph which is one method for determining the SIL as a measure of the effectiveness of signaling system. The purpose of this research is to make up for the weakness of the qualitative determination, which has input value ambiguity and a boundary problem in the SIL range. The fuzzy input valuable consists of consequence, exposure, avoidance and demand rate. The fuzzy inference produces forty eight fuzzy rule by adapting the calibrated risk graph in the IEC 61511. The Max-min composition is utilized for the fuzzy inference. The result of the fuzzy inference is the fuzzy value. Therefore, using the de-fuzzification method, the result should be converted to a crisp value that can be utilized for real projects. Ultimately, the safety requirement for hazard is identified by proposing a SIL result with a tolerable hazard rate. For the validation the results of the proposed method, the fuzzy risk graph model is compared with the safety analysis of the signaling system in CENELEC SC 9XA WG A10 report.

• Fire Science and Engineering
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• v.18 no.4
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• pp.42-51
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• 2004

This paper presents a study on the analysis of fire detection system using fuzzy logic with input variables of temperature and smoke density. The input variables for the fuzzy logic algorithm are measured by fire experiment of small scale with temperature detector and smoke detector. The antecedent part of fuzzy rules consists of temperature and smoke density, and the consequent part consists of fire possibility. Also the triangular fuzzy membership function is chosen for input variables and fuzzy rules to simplify computation. In order to calculate fuzzy values of such fuzzy system, a computer program is developed with Matlab based on graphics user interface. The experiment was conducted with paper and ethanol to simulate flaming fire and with plastic and sawdust to model smoldering fire. The results showed that the fire detection system presented here was able to diagnose fire very precisely. With the help of algorithms using fuzzy logic we could distinguish whether fire or not.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.511-518
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• 2009

A lot of methods are proposed to provide services for object informations in distributed domain to satisfy the recent increase of user-centered services. This paper proposed a method called fuzzy drop manager for the service of reliable distribution application domain objects. The proposed system accesses the domain using replica parameter ci,j and access matrix Z, and evaluates the reference relatedness inside the domain using the relatedness, given by the mapping of intra-domain fuzzy relevance, between fuzzy sets. Objects in the domain generated an $\alpha$-level set according to the reference relatedness obtained by applying $\alpha$-level to extend queries. Simulation results showed that the proposed method has better performance than the other methods.

• The Journal of Engineering Geology
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• v.11 no.2
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• pp.153-161
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• 2001

The quantitative evaluation of the stereo graphic projection, the limit equilibrium analysis, the finite difference analysis, the distinct element methocI is a analytical evaluation using many variables. Through the reliability analysis by the point estimation technique, uncertainty of other variables that have an effect on the stability of the rock slo~ was considered. The organized evaluation method of the approximate reasoning concept and using a fuzzy language was developed to confer and analysis the failure factors in planning and constructing the rock slope. Considering the result of the an- alysis, it was demonstrated that stability of entire sections can be evaluated through reliability analysis of point estimation technique. The results of stability evaluation by Fuzzy Approximate Reasoning is generally identical with the results of other existirw; analyses. As mentioned above, general and organized evaluation of special qualities of rock slope is possible using RMR Classification, Stereo Graphic Projection, Limit Equilibriwn Analysis, Finite Difference Analysis, Distinct Element Method, Point Estimation Technique, and Fuzzy Approximate Reasoning.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.99-105
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• 1998

This paper is to propose the fuzzy regression model using genetic algorithm which is fuzzy nonlinear regression model. Genetic algorithm is used to classify the input data for better fuzzy regression analysis. From this partition. each data can be have the grade of membership function which is belonged to a divided data group. The data group, from optimal partition of the region of each variable, have different fuzzy parameters of fuzzy linear regression model one another. We compound the fuzzy output of each data group so as to obtain the final fuzzy number for a data. We show the efficiency of this method by means of demonstration of a case study.

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1066-1073
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• 1998

In this paper, we propose a new efficient operator named DBAH (difference between arithmetic mean and harmonic mean) and a technique for extracting sketch features through learning fuzzy inference rules with a neural network. The DBAH operator provide some advantages; sensitivity dependence on local intensities and insensitivity on small rates of intensity change in very dark regions. Also, the proposed fuzzy reasoning technique by a neural network has a good performance in extracting sketch features without human intervention.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.799-803
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• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.355-360
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• 1999

In this paper, we propose a method that optimizes the parameters of fuzzy logic controller : centers and widths of membership functions and scaling factors using genetic algorithm. Before fuzzy logic controller controls a plant in real time, first off it is optimized by genetic algorithm. We select error and error variation between reference trajectory and real output for the input signals of fuzzy controller. We compared and investigated conventional fuzzy control method and proposed method through simulation and experiment using one link manipulator with nonlinear characteristic.