• 논리연구
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• 제12권1호
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• pp.89-110
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• 2009

This paper investigates generalizations of weakening-free uninorm logics not assuming associativity of intensional conjunction (so called fusion) &, as non-associative fuzzy-relevance logics. First, the strong t-associative monoidal uninorm logic StAMUL and its schematic extensions are introduced as non-associative propositional fuzzy-relevance logics. (Non-associativity here means that, differently from classical logic, & is no longer associative.) Then the algebraic structures corresponding to the systems are defined, and algebraic completeness results for them are provided. Next, predicate calculi corresponding to the propositional systems introduced here are considered.

• 논리연구
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• 제10권2호
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• pp.125-146
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• 2007

This paper investigates the relevance system R-mingle (RM) as a a fuzzy-relevance logic. It shows that RM is fuzzy in Cintula's sense, i.e., RM is complete with respect to linearly ordered L-matrices (or L-algebras). More exactly, we first introduce RM and its weak versions wwRM and wRM. We next provide algebraic and matrix completeness results for them.

• 논리연구
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• 제11권1호
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• pp.131-156
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• 2008

This paper first investigates several uninorm logics (introduced by Metcalfe and Montagna in [8]) as fuzzy-relevance logics. We first show that the uninorm logic UL and its extensions IUL, UML, and IUML are fuzzy-relevant; fuzzy in Cintula's sense, i.e., the logic L is complete with respect to linearly ordered L-matrices; and relevant in the weak sense that ${\Phi}{\rightarrow}{\Psi}$ is a theorem only if either (i) $\Phi$ and $\Psi$ share a sentential variable or constant, or (ii) both $\sim\Phi$ and $\Psi$ are theorems. We next expand these systems to those with $\triangle$.

• Journal of Power Electronics
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• 제16권3호
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• pp.1097-1109
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• 2016

Fault detection and isolation are related to system monitoring, identifying when a fault has occurred, and determining the type of fault and its location. Fault detection is utilized to determine whether a problem has occurred within a certain channel or area of operation. Fault detection and diagnosis have become increasingly important for many technical processes in the development of safe and efficient advanced systems for supervision. This paper presents an integrated technique for fault diagnosis and classification for open- and short-circuit faults in three-phase inverter circuits. Discrete wavelet transform and principal component analysis are utilized to detect the discontinuity in currents caused by a fault. The features of fault diagnosis are then extracted. A fault dictionary is used to acquire details about transistor faults and the corresponding fault identification. Fault classification is performed with a fuzzy logic system and relevance vector machine (RVM). The proposed model is incorporated with a set of optimization techniques, namely, evolutionary particle swarm optimization (EPSO) and cuckoo search optimization (CSO), to improve fault detection. The combination of optimization techniques with classification techniques is analyzed. Experimental results confirm that the combination of CSO with RVM yields better results than the combinations of CSO with fuzzy logic system, EPSO with RVM, and EPSO with fuzzy logic system.

• 논리연구
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• 제11권2호
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• pp.171-197
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• 2008

이 논문에서 우리는 [10]에서 멧칼페와 몬테그나에 의해 소개된 몇 체계들에 대한 새로운 표준 완전성 증명을 다룬다. 이를 위해 이 논문은 연관 논리 R-mingle (RM)의 이웃들로 간주될 수 있는 몇몇 퍼지-연관 논리를 연구한다. 우선, 좌-연속 항등적 멱등 유니놈들과 그것들의 잔여(left-continuous conjunctive idempotent uninorms and their residua)의 동어반복을 다루도록 의도된 monoidal uninorm idempotence 논리 MUIL과 그것의 몇몇 확장이 RM의 이웃으로 소개된다. 그리고 그것들에 상응하는 대수적 구조가 정의된 후, 이 체계들을 위한 표준 완전성 즉 단위 실수 [0, 1] 위에서의 완전성이 제공된다.

• 논리연구
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• 제15권2호
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• pp.207-222
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• 2012

이 글에서 우리는 연관 논리 R을 퍼지화한 체계 FR을 위한 크립키형 의미론을 다룬다. 이를 위하여 먼저 FR 체계를 소개하고 그에 상응하는 FR-대수를 정의한 후 FR이 대수적으로 완전하다는 것을 보인다. 다음으로 FR을 위한 대수적 크립키형 의미론을 소개하고 이를 대수적 의미론과 연관 짓는다. 마지막으로 이러한 의미론이 R에는 적용될 수 없다는 점을 보인다.

• 논리연구
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• 제22권2호
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• pp.291-307
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• 2019

이 글에서 우리는 연관 논리 R을 퍼지화한 체계 FR을 위한 집합 이론적인 크립키형 의미론을 다룬다. 이를 위하여 먼저 FR 체계와 그에 상응하는 크립키형 의미론을 소개한다. 다음으로 FR을 위한 집합 이론적 완전성 결과를 제공한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.9-14
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• 2002

Boolean retrieval is simple and elegant. However, since there is no provision for term weighting, no ranking of the answer set is generated. As a result, the size of the output might be too large or too small. Relevance feedback is the most popular query reformulation strategy. in a relevance feedback cycle, the user is presented with a list of the retrieved documents and, after examining them, marks those which are relevant. In practice, only the top 10(or 20) ranked documents need to be examined. The main idea consists of selecting important terms, or expressions, attached to the documents that have been identified as relevant by the user, and of enhancing the importance of these terms in a new query formulation. The expected effect is that the new query will be moved towards the relevant documents and away from the non-relevant ones. Local analysis techniques are interesting because they take advantage of the local context provided with the query. In this regard, they seem more appropriate than global analysis techniques. In a local strategy, the documents retrieved for a given query q are examined at query time to determine terms for query expansion. This is similar to a relevance feedback cycle but might be done without assistance from the user.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권3호
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• pp.157-162
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• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.