• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.230-236
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• 2011

This paper proposes the possibility which the fuzzy theory can be used to improve the performance of the parabolic SAR(Stop-And-Reverse) indicator in the trading systems for stock market. The simulation results with data of the KOSPI 200 future show that the occurred number of trading signals and the false signals in the proposed fuzzy SAR indicator is less than that in the conventional SAR indicator. In the conventional SAR system, the incremental value of the acceleration factor is usually setted as 0.02 and the maximum value of the acceleration factor is usually limited as 0.2. But in the proposed fuzzy SAR system, the incremental value and the maximum value of the acceleration factor are automatically adjusted by using the fuzzy rules, which are designed based-on the difference between short-term moving average and medium-term moving average and also based-on the slope of short-term moving average.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.84-89
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• 2003

Fuzzy controller is one of the succeed controller used in the process control in case of model uncertainties. But it my be difficult to fuzzy controller to articulate the accumulated knowledge to encompass all circumstance. Hence, it is essential to provide a tuning capability. There are many parameters in fuzzy controller can be adapted, scale factor tuning of normalized fuzzy controller is one of the adaptation parameter. Two adaptation methods are implemented in this work on an experimental thermal process, which simulate heating process in liquefied petroleum gases (LPG) recovery process in one of petrochemical industries: Gradient decent (GD) adaptation method; supervisory fuzzy controller. A comparison between the two methods is discussed.

• The Journal of the Acoustical Society of Korea
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• v.10 no.4
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• pp.38-47
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• 1991

The frequency variation among speakers is one of problems in the speech recognition. This paper applies fuzzy theory to solve the variation problem of frequency features. Reference patterns are expressed by fuzzified patterns which are produced by the peak frequency and the peak energy extracted from codebooks which are generated from training words uttered by several speakers, as they should include common features of speech signals. Words are recognized by fuzzy inference which uses the certainty factor between the reference patterns and the test fuzzified patterns which are produced by the peak frequency and the peak energy extracted from the power spectrum of input speech signals. Practically, in computing the certainty factor, to reduce memory capacity and computation requirements we propose a new equation which calculates the improved certainty factor using only the difference between two fuzzy values. As a result of experiments to test this word recognition method by fuzzy interence with Korean digits, it is shown that this word recognition method using the new equation presented in this paper, can solve the variation problem of frequency features and that the memory capacity and computation requirements are reduced.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.725-730
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• 1998

Recently we extended the fuzzy model for rule based systems incorporating an importance factor for each rule. The model permits for both unrestricted as well as non-negative importance factors. We use this extended model to design a fuzzy rule based classifier system which uses both the firing strength of the rule and the importance factor to decide the class label. The effectiveness of the scheme is established using several data sets.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.113-116
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• 2001

The conventional K-means algorithm is widely used in vector quantizer design and clustering analysis. Recently modified K-means algorithm has been proposed where the codevector updating step is as fallows: new codevector = current codevector + scale factor (new centroid - current codevector). This algorithm uses a fixed value for the scale factor. In this paper, we propose a new algorithm for the enhancement of learning time in fuzzy c-means a1gorithm. Experimental results show that the proposed method produces codebooks about 5 to 6 times faster than the conventional K-means algorithm with almost the same Performance.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.657-659
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• 1998

In order to optimize fuzzy modeling of nonlinear system, we proposed a optimal fuzzy model according to the characteristic of I/O relationship, HCM method, the genetic algorithm, and the objective function with weighting factor. A conventional fuzzy model has difficulty in definition of membership function. In order to solve its problem, the premise structure of the proposed fuzzy model is selected by both the partition of input space and the analysis of input-output relationship using the clustering algorithm. The premise parameters of the fuzzy model are optimized respectively by the genetic algorithm and the consequence parameters of the fuzzy model are identified by the standard least square method. Also, the objective function with weighting factor is proposed to achieve a balance between the performance results for the training and testing data.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.218-220
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• 1993

This paper deals with a self-tuning fuzzy controller. The fuzzy controller is constructed with linguistic rules which consist of the fuzzy variables and fuzzy sets. Each of fuzzy sets is characterized by a membership function. The tuning fussy controller has paramemters to effect control output. In this paper we propose tuning method for the scaling factor. Computer simulations carried out on a second-order process will show how the present tuning approach improves the transient and steady-state characteristics of the overall system.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.4
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• pp.111-118
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• 1995

This paper presents the modified fuzzy-compensated PID(FCPID) control, which involves adding the compensator to an existing PID controller, to improve the performances of the systems. Compared to a conventional PID control and a fuzzy logic control(FLC), the proposed control scheme has superior performance. Experimental results of an actual implementation of the modified PC-based FCPID controller on the DC servo-motor demonstrate considerable improve- ment of the performance of the existing FCPID control by monitoring the scaling factor. They show faster responses and smaller overshoots than the conventional FCPID control scheme for the various reference inputs and the robustness to the loads.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.897-899
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• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.261-264
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• 1992

In designing the fuzzy rule-based system, it has effected by the four significant factors such as the choice of membership function, scaling factor, the numbers of fuzzy control rule, the method of defuzzification. In this paper we design the fuzzy rule based system and evaluate by three factors, as followes reaching time, overshoot, and amplitude. And then we wiII show that the significant factors are the choice of scaling factor and the numbers of fuzzy control rule, and the system performance can be improved by the proper selection of the scaling factors.