• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.689-694
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• 2009

In this paper an approach to building fuzzy models for predicting daily and weekly stock prices is presented. Predicting stock prices with traditional time series analysis has proven to be difficult. Fuzzy logic based models have advantage of expressing the input-output relation linguistically, which facilitates the understanding of the system behavior. In building a stock prediction model we bear a burden of selecting most effective indicators for the stock prediction. In this paper information used in traditional candle stick-chart analysis is considered as input variables of our fuzzy models. The fuzzy rules have the premises and the consequents composed of trapezoidal membership functions and nonlinear equations, respectively. DE(Differential Evolution) identifies optimal fuzzy rules through an evolutionary process. The fuzzy models to predict daily and weekly open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) are built, and their performances are demonstrated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.11
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• pp.1202-1213
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• 2009

Muffler sound quality of a vehicle has widely been evaluated using commercial softwares. However, the commercial softwares are providing only simple sound quality index. To get better sound quality evaluation method of the muffler, a new approach is suggested based on the subjective sound quality test. The suggested subjective sound quality evaluation method is refined with the fuzzy theory. The developed method is applied for the evaluation of various kinds of vehicle muffler sounds.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes the hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.145-154
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• 1999

An intelligent system embedded with multiple sources of knowledge may provide more robust intelligence with highly ill structured problems than the system with a single source of knowledge. This paper proposes th hybrid knowledge integration mechanism that yields the cooperated knowledge by integrating expert, user, and machine knowledge within the fuzzy logic-driven framework, and then refines it with a genetic algorithm (GA) to enhance the reasoning performance. The proposed knowledge integration mechanism is applied for the prediction of Korea stock price index (KOSPI). Empirical results show that the proposed mechanism can make an intelligent system with the more adaptable and robust intelligence.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1892-1896
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• 2005

In this paper, an effective method for selecting significant input variables in building ANFIS (Adaptive Neuro-Fuzzy Inference System) for nonlinear system modeling is proposed. Dominant inputs in a nonlinear system identification process are extracted by evaluating the performance index and they are applied to ANFIS. The availability of our proposed model is verified with the Box and Jenkins gas furnace data. The comparisons with other methods are also given in this paper to show our proposed method is superior to other models.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.119-131
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• 2006

This paper proposes a mathematical model for web site service quality evaluation, which first applies analytic hierarchy process(AHP) to determine the weights of evaluation indexes of web site service quality and then analyzes web site service synthetically by means of fuzzy methodology. In this case, experts' knowledge cannot only be used but its subjective component can be eliminated. Hence, the web site service quality can be analyzed and evaluated more reasonably. After establishing this model, the experiment results will be given, which verify the feasibility and validity of the proposed model. The model proposed here is very simple and easy to implement and can provide a useful way to help developers evaluate their web site service quality efficiently.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.184-193
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• 1998

The existence of railroad line gives many influences in various kinds of aspects. To minimize negative influences is necessary to line's improvement, but appropriate methodology doesn't show until now. In this study, Fuzzy Integration Method(FIM) are employed in an effort to give ranking for railroad line's improvement in Seoul. The FIM is designed to generalize a various influences, appeared on account of existence of railroad line. Empirical analysis is performed for railroad line in distance of 83.5㎞ in Seoul. The total lines are divided in 51 sections, and there are selected a 11 evaluation index to reflect influences. Through a questionnaire survey about residents, operator and administrator, important degree of evaluation items are decided, reveal ins the interests of related groups. Then, evaluation values are calculated wi th practical survey results about each sections. The results of evaluation reveal that the higher ranking of improvement from FIM concentrates the Kyeng-Ul Line and Kyeng-Won line because these lines appear a many public discontents and negative influences such as noise and demolition of living environment.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3004-3006
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• 2000

In this paper, we propose an optimal design method of Fuzzy-Neural Networks model with parallel structure for complex and nonlinear systems. The proposed model is consists of a multiple number of FNN connected in parallel. The proposed FNNs with parallel structure is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. We use a HCM clustering and GAs to identify the structure and the parameters of the proposed model. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model. we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.