• Korean Journal of Mathematics
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• v.5 no.1
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• pp.1-8
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• 1997

In this note, we study on fuzzy uniform convergences of sequences of fuzzy numbers, and sequences of fuzzy functions.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.74-82
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• 2011

To do fuzzy modelling of a nonlinear process needs to analyze the characteristics of input-output of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods. For this, fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the fuzzy rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the clusters are used for identification of fuzzy model and membership functions are used as a series of triangular, gaussian-like, trapezoid-type membership functions. In the consequence part of the fuzzy rules fuzzy reasoning is conducted by two types of inferences such as simplified and linear inference. The identification of the consequence parameters, namely polynomial coefficients, of each rule are carried out by the standard least square method. And lastly, using gas furnace process which is widely used in nonlinear process we evaluate the performance and the system characteristics.

• Korean Journal of Mathematics
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• v.4 no.2
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• pp.95-100
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• 1996

In this note, the notion of fuzzy almost s-continuity is introduced and some results related to this notion are obtained.

• Honam Mathematical Journal
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• v.26 no.3
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• pp.355-364
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• 2004

In this paper, we consider fuzzy integrals defined by max-measures and discuss some properties of these fuzzy integrals of measurable functions.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.343-345
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• 2004

We propose a new category of fuzzy set-based fuzzy inference systems based on data granulation related to fuzzy space division for each variables. Data granules are viewed as linked collections of objects(data, in particular) drawn together by the criteria of proximity, similarity, or functionality. Granulation of data with the aid of Hard C-Means(HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polyminial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method. Numerical example is included to evaluate the performance of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.421-426
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes particle swarm optimization algorithm based optimal learning fuzzy-neural network (PSOA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by particle swarm optimization algorithm. The learning algorithm of the PSOA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, particle swarm optimization algorithm is used for tuning of membership functions of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1716-1722
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• 2005

Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes bacteria foraging algorithm based optimal learning fuzzy-neural network (BA-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by bacteria foraging algorithm. The learning algorithm of the BA-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, bacteria foraging algorithm is used for tuning of membership functions of the proposed model.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.956-958
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• 1995

This paper presents the automatic construction of fuzzy rule-based systems for diagnosing the faults of complex systems. Generally, fuzzy systems work well when we can use expert's experience to articulate fuzzy IF-THEN rules and memberships for fuzzy sets. When we cannot do this, we should generate the fuzzy rules and membership functions for fuzzy sets directly from experimental data. In this paper, we propose a new method on how to extract fuzzy sets and fuzzy rules. We also introduce an efficient fine-tunning algorithm of the parameters of membership functions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.3
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• pp.253-258
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informal speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.265-272
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• 1999

In this paper we define generalized concepts of cardinal-ity of a fuzzy-valued function and obtained some properties of these new concepts.