scholarly journals First-Order Mathematical Fuzzy Logic with Hedges

2016 ◽  
Author(s):  
Van-Hung Le
Keyword(s):  
Fuzzy Logic ◽  
First Order ◽  
Mathematical Fuzzy Logic

scholarly journals Syntactic characterizations of classes of first-order structures in mathematical fuzzy logic

2019 ◽  
Vol 23 (7) ◽  
pp. 2177-2186 ◽  
Author(s):  
Guillermo Badia ◽  
Vicent Costa ◽  
Pilar Dellunde ◽  
Carles Noguera
Keyword(s):  
Fuzzy Logic ◽  
First Order ◽  
Mathematical Fuzzy Logic ◽  
Order Structures

First-order fuzzy logic

Studia Logica ◽  
1987 ◽  
Vol 46 (1) ◽  
pp. 87-109 ◽  
Author(s):  
Vil�m Nov�k
Keyword(s):  
Fuzzy Logic ◽  
First Order

scholarly journals First-Order ARMA Type Fuzzy Time Series Method Based on Fuzzy Logic Relation Tables

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Cem Kocak
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Stock Exchange ◽  
Life Time ◽  
Fuzzy Time Series ◽  
Moving Averages ◽  
First Order ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Time Series Method

Fuzzy time series approaches have an important deficiency according to classical time series approaches. This deficiency comes from the fact that all of the fuzzy time series models developed in the literature use autoregressive (AR) variables, without any studies that also make use of moving averages (MAs) variables with the exception of only one study (Egrioglu et al. (2013)). In order to eliminate this deficiency, it is necessary to have many of daily life time series be expressed with Autoregressive Moving Averages (ARMAs) models that are based not only on the lagged values of the time series (AR variables) but also on the lagged values of the error series (MA variables). To that end, a new first-order fuzzy ARMA(1,1) time series forecasting method solution algorithm based on fuzzy logic group relation tables has been developed. The new method proposed has been compared against some methods in the literature by applying them on Istanbul Stock Exchange national 100 index (IMKB) and Gold Prices time series in regards to forecasting performance.

scholarly journals Answering Fuzzy Queries over Fuzzy DL-Lite Ontologies

2022 ◽  
pp. 1-30
Author(s):  
GABRIELLA PASI ◽  
RAFAEL PEÑALOZA
Keyword(s):  
Fuzzy Logic ◽  
Domain Knowledge ◽  
Description Logic ◽  
Classical Case ◽  
Point Of View ◽  
Query Answering ◽  
Data Complexity ◽  
Conjunctive Query ◽  
Triangular Norm ◽  
Mathematical Fuzzy Logic

Abstract A prominent problem in knowledge representation is how to answer queries taking into account also the implicit consequences of an ontology representing domain knowledge. While this problem has been widely studied within the realm of description logic ontologies, it has been surprisingly neglected within the context of vague or imprecise knowledge, particularly from the point of view of mathematical fuzzy logic. In this paper, we study the problem of answering conjunctive queries and threshold queries w.r.t. ontologies in fuzzy DL-Lite. Specifically, we show through a rewriting approach that threshold query answering w.r.t. consistent ontologies remains in ${AC}^{0}$ in data complexity, but that conjunctive query answering is highly dependent on the selected triangular norm, which has an impact on the underlying semantics. For the idempotent Gödel t-norm, we provide an effective method based on a reduction to the classical case.

scholarly journals Recognition of damaged letters based on mathematical fuzzy logic analysis

2015 ◽  
Vol 13 (2) ◽  
pp. 94-104 ◽  
Author(s):  
Vilém Novák ◽  
Petr Hurtík ◽  
Hashim Habiballa ◽  
Martin Štepnička
Keyword(s):  
Fuzzy Logic ◽  
Mathematical Fuzzy Logic ◽  
Fuzzy Logic Analysis
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