• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1624-1632
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• 2009

In this study, we introduce the design methodology of an optimized Interval Type-2 fuzzy controller. The fixed MF design of type-1 based FLC leads to the difficulty of rule-based control design for representing the linguistically uncertain expression. In the Type-2 FLC as the expanded type of Type-1 FLC, we can effectively improve the control characteristic by using the footprint of uncertainty(FOU) of membership function. Type-2 FLC has a robust characteristic in the unknown system with unspecific noise when compared with Type-1 FLC. Through computer simulation as well as practical experiment, we compare their performance by applying both the optimized Type-1 and Type-2 fuzzy cascade controllers to ball and beam system. To evaluate each controller performance, we consider controller characteristic parameters such as maximum overshoot, delay time, rise time, settling time and steady-state error.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.50-54
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• 1997

In general, the design of fuzzy logic controller has difficulties in the acquisition of expert's knowledge. So, some methods that can optimize the parameters for fuzzy logic controller automatically without expert knowledge was provided. Recently, tuning method for fuzzy logic controller using genetic algorithm(GA) were proposed in many papers. However, those are tuning methods for a part or some part of fuzzy logic controller. In this paper, we proposes auto tuning method for the whole part of tuzzy logic controller, such as parameters of membership functions for antecedence and consequence parts, rule base, scaling factor and the number of rule. Finally, second order dead time plant is provided to show the advantages of the proposed method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.507-509
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• 1999

In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.390-393
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• 1997

Fuzzy logic and neural networks are complemetary technologies in the design of intelligent system. Fuzzy neural network(FNN) as an auto-tuning method is actually known to an excellent method for the adjustment of the fuzzy rule. However, this has a weak point, because the convergence to the optimum depends on the initial condition. In this paper we develop a coding format to determine a FNN model by chromosome in GA and present systematic approach to identify the parameters and structure of FNN. The proposed hybrid tuning method realizes to construct minimal and optimal structure of the fuzzy mode simultaneously and automatically. This paper shows effectiveness of the tuning system by simulations compared with conventional methods.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.225-235
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• 2005

In this Paper, it was designed the GA-fuzzy controller of a Thyristor Controlled Series Capacitor(TCSC) for enhancement of power system stability. The newly designed controller of TCSC was designed to overcome the nonlinearity such as operating point change of power system as well as to respond to disturbances as uncertainties of line parameters and line fault. So, fuzzy controller by intelligent control theory was used for it. And the fuzzy controller was optimized from a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller namely. scaling factor. membership function and control rules. Nonlinear simulation results show that the proposed control technique is superior to conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of Multimedia Information System
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• v.6 no.2
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• pp.87-90
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• 2019

Machine learning techniques have been applied in almost all the domains of human life to aid and enhance the problem solving capabilities of the system. The field of medical science has improved to a greater extent with the advent and application of these techniques. Efficient expert systems using various soft computing techniques like artificial neural network, Fuzzy Logic, Genetic algorithm, Hybrid system, etc. are being developed to equip medical practitioner with better and effective diagnosing capabilities. In this paper, a comparative study to evaluate the predictive performance of subtractive clustering based ANFIS hybrid system (SCANFIS) with Fuzzy C-Means (FCM) based ANFIS system (FCMANFIS) for Alzheimer disease (AD) has been taken. To evaluate the performance of these two systems, three parameters i.e. root mean square error (RMSE), prediction accuracy and precision are implemented. Experimental results demonstrated that the FCMANFIS model produce better results when compared to SCANFIS model in predictive analysis of Alzheimer disease (AD).

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.171-175
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• 2007

In this paper a methodology has been proposed to compute the parameters of the multilayer earth model using a genetic algorithm(GA). The results provided by the GA constitute the indispensable data that can be used in circuital or field simulations of grounding systems. This methodology allows to proceed toward a very efficient simulation of the grounding system and an accurate calculation of potential on the ground's surface. The sets of soil resistivity used for GA are measured in Jeju area.

• Journal of Navigation and Port Research
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• v.34 no.2
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• pp.117-122
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• 2010

Since fuzzy controllers are nonlinear, it is more difficult to set the controller gains and to analyse the stability compared to conventional PID controllers. This paper proposes a fuzzy PD+I controller for tracking control which uses a linear fuzzy inference(product-sum-gravity) method based on a conventional linear PID controller. In this scheme the fuzzy PD+I controller works similar to the control performance as the linear PD plus I(PD+I) controller. Thus it is possible to analyse and design an fuzzy PD+I controller for given systems based on a linear fuzzy PD controller. The scaling factors tuning scheme, another topic of fuzzy controller design procedure, is also introduced in order to fine performance of the fuzzy PD+I controller. The scaling factors are adjusted by a real-coded genetic algorithm(RCGA) in off-line. The simulation results show the effectiveness of the proposed fuzzy PD+I controller for tracking control problems by comparing with the conventional PID controllers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.52-57
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• 2003

In this paper, we propose a Fuzzy Classifier System(FCS) to find a set of fuzzy rules which can carry out the edge detection. The classifier system of Holland can evaluate the usefulness of rules represented by classifiers with repeated learning. FCS makes the classifier system be able to carry out the mapping from continuous inputs to outputs. It is the FCS that applies the method of machine learning to the concept of fuzzy logic. It is that the antecedent and consequent of classifier is same as a fuzzy rule. In this paper, the FCS is the Michigan style. A single fuzzy if-then rule is coded as an individual. The average gray levels which each group of neighbor pixels has are represented into fuzzy set. Then a pixel is decided whether it is edge pixel or not using fuzzy if-then rules. Depending on the average of gray levels, a number of fuzzy rules can be activated, and each rules makes the output. These outputs are aggregated and defuzzified to take new gray value of the pixel. To evaluate this edge detection, we will compare the new gray level of a pixel with gray level obtained by the other edge detection method such as Sobel edge detection. This comparison provides a reinforcement signal for FCS which is reinforcement learning. Also the FCS employs the Genetic Algorithms to make new rules and modify rules when performance of the system needs to be improved.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.167-173
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• 2002

In this paper, we propose an interactive genetic algorithm (IGA) to implement an automated 2D avatar synthesis. The IGA technique is capable of expressing user's personality in the avatar synthesis by using the user's response as a candidate for the fitness value. Our suggested IGA method is applied to creating avatars automatically. Unlike the previous works, we introduce the concepts of 'hidden population', as well as 'primitive avatar' and 'simplified genotype', which are used to overcome the shortcomings of IGA such as human fatigue or reliability, and reasonable rates of convergence with a less number of iterations. The procedure of designing avatar models consists of two steps. The first step is to detect the facial feature points and the second step is to create the subjectively optimal avatars with diversity by embedding user's preference, intuition, emotion, psychological aspects, or a more general term, KANSEI. Finally, the combined processes result in human-friendly avatars in terms of both genetic optimality and interactive GUI with reliability.