• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.247-252
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• 1999

The purposed of this paper is to introduced some basic concepts of Galois connection between fuzzy ordered sets. And discuss its relations with the property of fuzzy ordered set.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.511-513
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• 2005

We investigate some situations in connection with two exact sequences of fuzzy linear maps.

• Advances in Computational Design
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• v.2 no.1
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• pp.29-42
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• 2017

This study presents algorithms for determining the fuzzy critical loads of planar steel frame structures with fixity factors of beam - column and column - base connections are modeled as triangular fuzzy numbers. The finite element method with linear elastic semi-rigid connection and Response Surface Method (RSM) in mathematical statistic are applied for problems with symmetric triangular fuzzy numbers. The ${\alpha}$ - level optimization using the Differential Evolution (DE) involving integrated finite element modeling is proposed to apply for problems with any triangular fuzzy numbers. The advantage of the proposed methodologies is demonstrated through some example problems relating to for the twenty - story, four - bay planar steel frames.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.635-639
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the modeling of chaotic nonlinear systems. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the modeling performance for chaotic nonlinear systems and compare it with those of the FNN and the WFM.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.111-118
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• 2004

In this paper, we present a novel approach for the structure of Fuzzy Neural Network(FNN) based on wavelet function and apply this network structure to the solution of the tracking problem for mobile robots. Generally, the wavelet fuzzy model(WFM) has the advantage of the wavelet transform by constituting the fuzzy basis function(FBF) and the conclusion part to equalize the linear combination of FBF with the linear combination of wavelet functions. However, it is very difficult to identify the fuzzy rules and to tune the membership functions of the fuzzy reasoning mechanism. Neural networks, on the other hand, utilize their learning capability for automatic identification and tuning. Therefore, we design a wavelet based FNN structure(WFNN) that merges these advantages of neural network, fuzzy model and wavelet transform. The basic idea of our wavelet based FNN is to realize the process of fuzzy reasoning of wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. And our network can automatically identify the fuzzy rules by modifying the connection weights of the networks via the gradient descent scheme. To verify the efficiency of our network structure, we evaluate the tracking performance for mobile robot and compare it with those of the FNN and the WFM.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.592-594
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• 2009

In this paper we consider voting analysis in the political science in connection with $B_n$(or $M_n${0, 1}), the semigroup of the binary relations on X with n elements. We also consider it in connection with $M_n$(F) (or $B_n$(F)), the semigroup of all fuzzy binary relations on X. Also we establish a possibility theorem and an impossibility theorem in voting analysis based on preferences in $B_n$ and $M_n$(F).

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.106-109
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• 1997

In this paper we define the t-fuzzy equivalence relation on a set and we prove some properties in connection with t-fuzzy relations.

• Honam Mathematical Journal
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• v.44 no.4
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• pp.535-559
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• 2022

The notions of cubic intuitionistic fuzzy set to filters and implicative filters of BE-algebras are introduced. Relations between cubic intuitionistic fuzzy filters with cubic intuitionistic fuzzy implicative filters of BE-algebras are investigated. The homomorphic image and inverse image of cubic intuitionistic fuzzy filters are studied and some related properties are investigated. Also, the product of cubic intuitionistic fuzzy subalgebras (implicative filters) of BE-algebras are investigated.

• Structural Engineering and Mechanics
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• v.47 no.5
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• pp.679-700
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• 2013

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.419-422
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• 2003

To improve the recognition accuracy of a developed artificial odor discrimination system for three mixture fragrance recognition, Fuzzy Similarity based Self-Organized Network inspired by Immune Algorithm (F-SONIA) is proposed. Minimum, average, and maximum values of fragrance data acquisitions are used to form triangular fuzzy numbers. Then the fuzzy similarity treasure is used to define the relationship between fragrance inputs and connection strengths of hidden units. The fuzzy similarity is defined as the maximum value of the intersection region between triangular fuzzy set of input vectors and the connection strengths of hidden units. In experiments, performances of the proposed method is compared with the conventional Self-Organized Network inspired by Immune Algorithm (SONIA), and the Fuzzy Learning Vector Quantization (FLVQ). Experiments show that F-SONIA improves recognition accuracy of SONIA by 3-9％. Comparing to the previously developed artificial odor discrimination system that used FLVQ as pattern classifier, the recognition accuracy is increased by 14-25％.