• Korean Journal of Logic
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• v.12 no.1
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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.

• Korean Journal of Logic
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• v.10 no.2
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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.

• Korean Journal of Logic
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• v.11 no.1
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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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• v.16 no.3
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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.

• Korean Journal of Logic
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• v.11 no.2
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• pp.171-197
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• 2008

In this paper we deal with new standard completeness proofs of some systems introduced by Metcalfe and Montagna in [10]. For this, this paper investigates several fuzzy-relevance logics, which can be regarded as neighbors of the R of Relevance with mingle (RM). First, the monoidal uninorm idempotence logic MUIL, which is intended to cope with the tautologies of left-continuous conjunctive idempotent uninorms and their residua, and some schematic extensions of it are introduced as neighbors of RM. The algebraic structures corresponding to them are defined, and standard completeness, completeness on the real unit interval [0, 1], results for them are provided.

• Korean Journal of Logic
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• v.15 no.2
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• pp.207-222
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• 2012

This paper deals with Kripke-style semantics for FR, a fuzzy version of R of Relevance. For this, first, we introduce FR, define the corresponding algebraic structures FR-algebras, and give algebraic completeness results for it. We next introduce an algebraic Kripke-style semantics for FR, and connect it with algebraic semantics. We furthermore show that such semantics does not work for R.

• Korean Journal of Logic
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• v.22 no.2
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• pp.291-307
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• 2019

This paper deals with set-theoretic Kripke-style semantics for FR, a fuzzy version of R of Relevance. For this, first, we introduce the system FR and its corresponding Kripke-style semantics. Next, we provide set-theoretic completeness results for it.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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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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• v.16 no.3
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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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• v.14 no.2
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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.