• 대한수학회논문집
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• 제27권4호
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• pp.669-687
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• 2012

As a generalization of the concept of a fuzzy implicative filter which is introduced by Liu and Li [3], the notion of (${\in}$, ${\in}{\vee}q_k$)-fuzzy implicative filters is introduced, and related properties are investigated. The relationship between (${\in}$, ${\in}{\vee}q_k$)-fuzzy filters and (${\in}$, ${\in}{\vee}q_k$)-fuzzy implicative filters is established. Conditions for an (${\in}$, ${\in}{\vee}q_k$)-fuzzy filter to be an (${\in}$, ${\in}{\vee}q_k$)-fuzzy implicative filter are considered. Characterizations of an (${\in}$, ${\in}{\vee}q_k$)-fuzzy implicative filter are provided, and the implication-based fuzzy implicative filters of an $R_0$-algebra is discussed.

• Kyungpook Mathematical Journal
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• 제51권1호
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• pp.11-28
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• 2011

The notions of ($\overline{\in}$, $\overline{\in}{\vee}\overline{qk}$)-fuzzy BCK-filters and fuzzy BCK-filters with thresholds are introduced, and several related properties are investigated. Characterizations of such notions are displayed, and implication-based fuzzy BCK-filters are discussed.

• 전자공학회논문지B
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• 제31B권6호
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• pp.43-52
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• 1994

A design method of rule-based fuzzy modeling is presented for the model identification of complex and nonlinear systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient form of ``IF....,THEN...', using the theories of optimization theory , linguistic fuzzy implication rules and fuzzy c-means clustering. Three kinds of method for fuzzy modeling presented in this paper include simplified inference (type I), linear inference (type 2), and modified linear inference (type 3). In order to identify premise structure and parameter of fuzzy implication rules, fuzzy c- means clustering and modified complex method are used respectively and the least sequare method is utilized for the identification of optimum consequence parameters. Time series data for gas furance and those for sewage treatment process are used to evaluate the performance of the proposed rule-based fuzzy modeling. Comparison shows that the proposed method can produce the fuzzy model with higher accuracy than previous other studies.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.222-230
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• 2003

의료 시스템이 개발되면 시스템이 사용하는 knowledge source의 신뢰도가 시스템의 수행능력에 큰 영향을 미치게 되므로, knowledge source의 신뢰도를 검증해야한다. 본 논문은 의료 시스템 CLINAID의 knowledge source의 신뢰성 조사에 대한 연구의 방법과 결과를 발표하였다. 그 방법으로는 CLINAID에 사용된 Cardiovascular body system 데이터에 fuzzy relational method를 적용하여 구조적 분석을 통해 만들어진 인공의 syndrome을 knowledge base에 저장되어있는 의료 전문가의 syndrome과 비교하였다. 7 가지 fuzzy implication operator를 사용하여 거의 비슷한 결과들을 산출해 냈으며, 그 결과들이 전문가가 제공한 syndrome과 거의 일치하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.146-151
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• 1994

An empirical comparison of static fuzzy relational models which are identified with different fuzzy implication operators and inferred by different composition operators is made in case that all the information is represented by the fuzzy discretization. Four performance measures (integral of mean squared error, maximal error, fuzzy equality index and mean lack of sharpness) are adopted to evaluate and compare the quality of the fuzzy relational models both at the numerical level and logical level. As the results, the fuzzy implication operators useful in various fuzzy modeling problems are discussed and it is empirically shown that the selection of data pairs is another important factor for identifying the fuzzy model with high quality.

• Journal of applied mathematics & informatics
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• 제26권3_4호
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• pp.653-660
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• 2008

In this paper, we consider the fuzzification of prime filters in Lattice Implication Algebras (briefly, LIAs), and introduce the concepts of fuzzy prime filters (briefly, FP-filters), and we also studied the properties of FP-filters. Finally, we investigate the properties of fuzzy prime dual filters (briefly, FPD-filters) in LIA, and the relations of them are investigated.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.849-855
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• 2008

The Diagnostic Unit of CLINAID can infer working diagnoses for general diseases from the information provided by a user. This user-provided information in the form of signs and symptoms, however, is usually not sufficient to make a final decision on a working diagnosis. In order for the Diagnostic Unit to reach a diagnostic conclusion, it needs to select suitable clinical investigations for the patients. Because different investigations can be selected for the same patient, we need a process that can optimize the selection procedure employed by the Diagnostic Unit. This process, called a selection algorithm, must work with the fuzzy relational method because CLINAID uses fuzzy relational structures extensively for its knowledge bases and inference mechanism. In this paper we present steps of the selection algorithm along with simulation results on this algorithm using fuzzy relational products, both harsh product and mean product. The computation results of applying several different fuzzy implication operators are compared and analyzed.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1995년도 추계학술대회 학술발표 논문집
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• pp.153-158
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• 1995

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed fuzzy GMDH modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) algorithm 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 algorithm and fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnaceare those for sewage treatment process are used for the purpose of evaluating the performance of the proposed fuzzy GMDH modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• 대한수학회논문집
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• 제26권3호
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• pp.385-403
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• 2011

Generalizations of the notion of fuzzy hyper K-subalgebras are considered. The concept of fuzzy hyper K-subalgebras of type (${\alpha},{\beta}$) where ${\alpha}$, ${\beta}$ ${\in}$ {${\in}$, q, ${\in}{\vee}q$, ${\in}{\wedge}q$} and ${\alpha}{\neq}{\in}{\wedge}q$. Relations between each types are investigated, and many related properties are discussed. In particular, the notion of (${\in}$, ${\in}{\vee}q$)-fuzzy hyper K-subalgebras is dealt with, and characterizations of (${\in}$, ${\in}{\vee}q$)-fuzzy hyper K-subalgebras are established. Conditions for an (${\in}$, ${\in}{\vee}q$)-fuzzy hyper K-subalgebra to be an (${\in}$, ${\in}$)-fuzzy hyper K-subalgebra are provided. An (${\in}$, ${\in}{\vee}q$)-fuzzy hyper K-subalgebra by using a collection of hyper K-subalgebras is established. Finally the implication-based fuzzy hyper K-subalgebras are discussed.

• 한국콘텐츠학회논문지
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• 제11권3호
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• pp.73-83
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• 2011

질의 응답 시스템은 사용자의 질의에 대해 다른 사용자의 응답을 저장하고 보여 주는 시스템이다. 사용자의 질의를 만족시키는 응답을 정확히 검색하고자 노력하는 많은 연구들이 있었지만 이에는 근본적인 한계가 있었다. 따라서 질의 응답 시스템에서는 보조적인 방법으로 사용자의 질의를 만족시킬 가능성이 높은 다른 질의를 추천하는 방법이 사용되고 있다. 이전 연구에서 내용적으로 포함하는 정도가 큰 질의들을 하위 질의로서 추천하는 내용 기반 추천 방법으로서 퍼지 관계 곱 연산자(fuzzy relational product operator)를 사용하는 방법이 제안되었고, 기본적인 함의 연산자로서 Kleene-Dienes 연산자가 사용되었다. 하지만 Kleene-Dienes 연산자는 설명의 의미적 포함관계를 고려한 방법이 아니기 때문에 질의응답의 의미적 포함 정도를 계산하기에 적합하지 않다. 본 논문에서는 두 질의에 대한 설명의 의미적 포함관계를 고려한 새로운 함의 연산자를 제안한다. 새로운 연산자는 어떤 질의 및 응답 들이 다른 질의와 그 응답들에 의미적으로 포함되는 정도를 계산하도록 설계되었다. 실험을 통하여 새로운 함의 연산자를 적용한 퍼지 관계곱 연산자를 사용하면 사용자가 원하는 지식을 추천할 가능성이 높아짐을 보였다.