• Honam Mathematical Journal
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• v.31 no.3
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• pp.323-333
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• 2009

We introduce the concepts of a fuzzifying filter and a fuzzifying implicative filter in lattice implication algebras.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.424-431
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• 2012

In information retrieval, search engine provide the rank of web page and the summary of the web page information to user. Snippet is a summaries information of representing web pages. Visiting the web page by the user is affected by the snippet. User sometime visits the wrong page with respect to user intention when uses snippet. The snippet extraction method is difficult to accurate comprehending user intention. In order to solve above problem, this paper proposes a new snippet extraction method using fuzzy implication operator and relevance feedback. The proposed method uses relevance feedback to expand the use's query. The method uses the fuzzy implication operator between the expanded query and the web pages to extract snippet to be well reflected semantic user's intention. The experimental results demonstrate that the proposed method can achieve better snippet extraction performance than the other methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1181-1186
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• 1993

In the paper, a new design method of rule-based fuzzy modeling is proposed for model identification of nonlinear systems. The structure indentification is carried out, utilizing fuzzy c-means clustering. Fuzzy-neural networks composed back-propagation algorithm and linear fuzzy inference method, are used to identify parameters of the premise and consequence parts. To obtain optimal linguistic fuzzy implication rules, the learning rates and momentum coefficients are tuned automatically using a modified complex method.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Kyungpook Mathematical Journal
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• v.53 no.3
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• pp.435-457
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• 2013

Using the Lukasiewicz 3-valued implication operator, the notion of an (${\alpha},{\beta}$)-intuitionistic fuzzy left (right) $h$-ideal of a hemiring is introduced, where ${\alpha},{\beta}{\in}\{{\in},q,{\in}{\wedge}q,{\in}{\vee}q\}$. We define intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) of a hemiring R and investigate their various properties. We characterize intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) and (${\alpha},{\beta}$)-intuitionistic fuzzy left (right) $h$-ideal of a hemiring R by its level sets. We establish that an intuitionistic fuzzy set A of a hemiring R is a (${\in},{\in}$) (or (${\in},{\in}{\vee}q$) or (${\in}{\wedge}q,{\in}$)-intuitionistic fuzzy left (right) $h$-ideal of R if and only if A is an intuitionistic fuzzy left (right) $h$-ideal with thresholds (0, 1) (or (0, 0.5) or (0.5, 1)) of R respectively. It is also shown that A is a (${\in},{\in}$) (or (${\in},{\in}{\vee}q$) or (${\in}{\wedge}q,{\in}$))-intuitionistic fuzzy left (right) $h$-ideal if and only if for any $p{\in}$ (0, 1] (or $p{\in}$ (0, 0.5] or $p{\in}$ (0.5, 1] ), $A_p$ is a fuzzy left (right) $h$-ideal. Finally, we prove that an intuitionistic fuzzy set A of a hemiring R is an intuitionistic fuzzy left (right) $h$-ideal with thresholds ($s,t$) of R if and only if for any $p{\in}(s,t]$, the cut set $A_p$ is a fuzzy left (right) $h$-ideal of R.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.730-737
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• 2001

In this paper, an stabilization control method using adaptive neuro-fuzzy controller(ANFC) is proposed for modeling of nonlinear complex systems. The proposed adaptive neuro-fuzzy controller implements system structure and parameter identification using the intelligent schemes together with optimization theory, linguistic fuzzy implication rules, and neural networks from input and output data of processes. The results show that the proposed method can produce the intelligence model with higher accuracy than other works achieved previously.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.81-89
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• 1996

A design method of fuzzy modeling is presented for the model identification of route choice of traffic problems.The proposed fuzzy modeling implements system structure and parameter identification in the eficient form of""IF..., THEN-.."", using the theories of optimization theory, linguistic fuzzy implication rules. Three kinds ofmethod for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 21,and proposed modified-linear inference (type 3). The fuzzy inference method are utilized to develop the routechoice model in terms of accurate estimation and precise description of human travel behavior. In order to identifypremise structure and parameter of fuzzy implication rules, improved complex method is used and the least squaremethod is utilized for the identification of optimum consequence parameters. Data for route choice of trafficproblems are used to evaluate the performance of the proposed fuzzy modeling. The results show that the proposedmethod can produce the fuzzy model with higher accuracy than previous other studies -BL(binary logic) model,B(production system) model, FL(fuzzy logic) model, NN(neura1 network) model, and FNNs (fuzzy-neuralnetworks) model -.fuzzy-neural networks) model -.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.417-422
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• 1998

This paper presents a method of object classification from dynamic image based on fuzzy inference algorithm which is suitable for low speed such as, conveyor, uninhabited transportation. At first, by using feature parameters of moving object, fuzzy if - then rule that can be able to adapt the wide variety of surroundings is developed. Secondly, implication function for fuzzy inference are compared with respect the proposed algorithm. Simulation results are presented to testify the performance and applicability of the proposed system.

• Journal of the Korean Institute of Intelligent Systems
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• v.3 no.1
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• pp.15-22
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• 1993

• Communications of the Korean Mathematical Society
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• v.22 no.2
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• pp.173-181
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• 2007

Using the belongs to relation ($\in$) and quasi-coincidence with relation (q) between fuzzy points and fuzzy sets, the concept of ($\alpha,\;\beta$)-fuzzy subalgebras where $\alpha,\;\beta$ are any two of $\{{\in},\;q,\;{\in}\;{\vee}\;q,\;{\in}\;{\wedge}\;q\}$ with ${\alpha}\;{\neq}\;{\in}\;{\wedge}\;q$ was introduced, and related properties were investigated in [3]. As a continuation of the paper [3], in this paper, the notion of a fuzzy subalgebra with thresholds is introduced, and its characterizations are obtained. Relations between a fuzzy subalgebra with thresholds and an (${\in},\;{\in}\;{\vee}\;q$)-fuzzy subalgebra are provided.