• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.303-308
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• 2006

To solve the rule explosion problem in multi input fuzzy logic system, a method of converting a given fuzzy system to 2 layered hierarchical fuzzy system is presented where the collection of the THEN-parts of the fuzzy rules of given fuzzy system is considered as vectors of fuzzy rule. At the 1 st layer, linearly independent fuzzy rule vectors generated from the given fuzzy logic system are used and, at the 2nd layer, linear combinations of these independent fuzzy rule vectors are used for fuzzy logic units at each layer. The resultant 2 layered hierarchical fuzzy system has not only equivalent approximation capability, but less number of fuzzy rules compared with the conventional fuzzy logic system.

• Journal of Biosystems Engineering
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• v.28 no.2
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.284-284
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• 2000

In adaptive fuzzy control systems. fuzzy systems are used to approximate the unknown plant nonlinearities. Until now. most of the papers in the field of controller design for nonlinear system using fuzzy systems considers the affine system with fixed grid-rule structure based on system state availability. This paper considers observer-based nonlinear controller and dynamic fuzzy rule structure. Adaptive laws for fuzzy parameters for state observer and fuzzy rule structure are established so that the whole system is stable in the sense of Lyapunov.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.155-156
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• 2008

In this paper, an interval type-2 fuzzy logic system is designed and compared with a type-1 fuzzy logic system. To compare performance of a type-1 fuzzy logic system with the type-2 fuzzy logic system, we apply type-1 fuzzy logic system and type-2 system to modeling the noised data. Membership function of interval type-2 fuzzy logic system is designed consequents of rules including uncertainty. For general type-2 fuzzy logic system computational complexity is severe. On the other hand, theoretic and arithmetic computations for interval type-2 fuzzy logic systems are very simple.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.57-62
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• 2003

It is difficult to determine the feeding rate of coagulant in the water treatment plant, due to nonlinearity, multivariables and slow response characteristics etc. To deal with this difficulty, the genetic-fuzzy system genetic-equation system and the neural network system were used in determining the feeding rate of the coagulant. Fuzzy system and neural network system are excellently robust in multivariables and nonlinear problems. but fuzzy system is difficult to construct the fuzzy parameter such as the rule table and the membership function. Therefore we made the genetic-fuzzy system by the fusion of genetic algorithms and fuzzy system, and also made the feeding rate equation by genetic algorithms. To train fuzzy system, equation parameter and neural network system, the actual operation data of the water treatment plant was used. We determined optimized feeding rates of coagulant by the fuzzy system, the equation and the neural network and also compared them with the feeding rates of the actual operation data.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.553-561
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• 2007

In this paper we propose the concept of fuzzy basis of fuzzy submachine, which is the generalized form of crisp basis of submachine, and we extend the system of generators and free subset to fuzzy forms, from which we prove that minimal system of fuzzy generators, maximally free fuzzy subset, and fuzzy basis are equivalent forms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.5-8
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• 2003

This paper we study the exact controllability for the nonlinear fuzzy control system in E$_{N}$$^{n}$ by using the concept of fuzzy number of dimension n whose values are normal, convex, upper semicontinuous and compactly supported surface in R$^{n}$ . fuzzy number of dimension n ; fuzzy control ; nonlinear fuzzy control system ; exact controllabilityty

• Communications of the Korean Mathematical Society
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• v.26 no.2
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• pp.169-182
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• 2011

The notions of bipolar fuzzy hyper MV-subalgebras, (weak) bipolar fuzzy hyper MV-deductive system and precisely weak bipolar fuzzy hyper MV-deductive system are introduced, and their relations are investigated. Characterizations of bipolar fuzzy hyper MV-subalgebras and weak bipolar fuzzy hyper MV-deductive systems are provided.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.387-390
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• 1998

In this paper, we suggested a neuro-fuzzy learning algorithm for tuning fuzzy rules, in which a fuzzy system model is of additive-type. Using the method, it is possible to reduce the computation size, since performing the fuzzy inference and tuning the fuzzy rules of each fuzzy subsystem model are independent. Moreover, the efficiency of suggested method is shown by means of a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.367-367
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian membership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.