• Journal of the Korea Society of Computer and Information
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• v.1 no.1
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• pp.159-170
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• 1996

In this paper, we propose a generating method for the optimal rules for the nonlinear control system using evolution algorithms and fuzzy controller. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and. knowledge. and ran be intelligent control. The approachpresented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which Is tile defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method In non -linear systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.83-86
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• 2001

In this paper, it study about the image filter applied the hybrid fuzzy membership function to the neuro-fuzzy system. Here, this system applys the genetic algorithm in order to obtain the optimal image as the iteration carry for making the data value in the error. It is removed the included noise in an image using the proposed image filter and compared the proposed image filter performance with the other filters using MATLAB. And it is found that the proposed filter performance is superior to the other filters which has the similar structure through the images. To show the superior ability, it is compared with MSE and SNR for images.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.228-231
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• 2000

A hybrid fuzzy controller by means of the genetic algorithms is presented. The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PlD's output in steady state by a fuzzy variable. The HFC combined a PID controller with a fuzzy controller concurrently produces the better output performance than any other controller. A auto-tuning algorithms is presented to automatically improve the performance of hybrid fuzzy controller using genetic algorithms. The algorithms estimates automatical Iy the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules. Especially, in order to auto-tune scaling factors and PID parameters of HFC using GA three kinds of estimation modes are effectively utilized. The HFCs are applied to the second process with time-delay. Computer simulations are conducted at step input and the performances of systems are evaluated and also discussed in ITAE(Integral of the Time multiplied by the Absolute value of Error ) and other ways.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.491-493
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• 1995

In this paper, the method of long-term load forecasting using a fuzzy neural network of which input is a fuzzy membership function value of a input variable like as GNP which is considered to affect demand of load. The proposed method was applicated in Korea Electric Power Corporation (KEPCO). The comparison with Error Back-Propagation Neural Network has been shown.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3019-3021
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• 2000

In this paper. a new fuzzy modeling algorithm is proposed : it can express a given unknown system with a small number of fuzzy rules and be easily implemented. This method uses an independent array instead of a lattice form for a premise membership function. For the purpose of getting the initial value of fuzzy rules. the method uses the fuzzy c-means clustering method. To optimally tune the initial fuzzy rule. the DNA coding method is also utilized at same time. Box and Jenkins's gas furnace data is used to illustrate the validity of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1230-1233
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• 1993

Numerical solution of inverse problem of Takagi-Sugeno fuzzy model is proposed. The method is located on the application of numerical optimization to the fuzzy model. Steepest descent method is used for the numerical optimization. We use the linear approximation of fuzzy model, called pseudo first order approximation, by fixing the membership value on the neighborhood of the corresponding input. It is introduced in order to reduce the difficulty of optimization process. The efficiency of this method is shown by a numerical experiment.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1281-1284
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• 1993

By the exponential representation form (EF) for fuzzy logic, any fuzzy value a (in fuzzy valued logic or fuzzy linguistic valued logic) can be represented as Bc, where B is called the truth base and C the confidence exponent. This paper will propose the basic concepts of this form and discuss its interesting properties. By using a different truth base, the exponential form can be used to represent the positive and the negative logic in fuzzy valued logic as well as in fuzzy linguistic valued logic. Some Simple application examples of EF for approximate reasoning are also illustrated in this paper.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.968-971
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• 1993

Fuzzy inference system which inferences and processes the Fuzzy information is designed using digital voltage mode neural circuits. The digital fuzzification circuit is designed to MIN,MAX circuit using CMOS neural comparator. A new defuzzification method which uses the center of area of the resultant fuzzy set as a defuzzified output is suggested. The method of the center of area(C. O. A) search for a crisp value which is correspond to a half of the area enclosed with inferenced membership function. The center of area defuzzification circuit is proposed. It is a simple circuit without divider and multiflier. The proposed circuits are verified by implementing with conventional digital chips.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1177-1180
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• 1993

In this paper, a fuzzy-neural interpolating network is proposed to efficiently approximate a nonlinear function. Specifically, basis functions are first constructed by Fuzzy Membership Function based Neural Networks (FMFNN). And the fuzzy similarity, which is defined as the degree of matching between actual output value and the output of each basis function, is employed to determine initial weighting of the proposed network. Then the weightings are updated in such a way that square of the error is minimized. To show the capability of function approximation of the proposed fuzzy-neural interpolating network, a numerical example is illustrated.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.73-83
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• 1995

In this paper, we propose a generating method for the optimal rules of the fuzzy rule base using evolution algorithms. With the aid of evolution algorithms optimal rules of fuzzy logic system can be automatic designed without human expert's priori experience and knowledge. can be intelligent control. The a, pp.oach presented here generating rules by self-tuning the parameters of membership functions and searchs the optimal control rules based on a fitness value which is the defined performance criterion. Computer simulations demonstrates the usefulness of the proposed method in non-linear systems.