Fuzzy time series forecasting methods do not require constraints found in conventional approaches. In addition, due to uncertainty that they contain, many time series to be forecasted should be considered as fuzzy time series. Fuzzy time series forecasting models consist of three steps as fuzzification, identification of fuzzy relations and defuzzification. Although most of the time series enco...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.040 ⇑ Corresponding author. Tel.: +90 312 2977900. E-mail address: [email protected] (C.H. Alad In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural net...

Fuzzy linguistic quantifiers – operators intended to model vague quantifying expressions in natural language like “almost all” or “few” – have gained importance as operators for information combination and data summarisation. They are particularly appealing because of their ease-of-use: people are familiar with these operators, which can be applied for technical aggregation purposes in the same...

This paper is concerned with the foundations of fuzzy reasoning and its relationships with other logics of uncertainty. The definitions of fuzzy logics are first examined and the role of fuzzification discussed. It is shown that fuzzification of PC gives a known multivalued logic but with inappropriate semantics of implication and various alternative forms of implication are discussed. In the m...

Fuzzy cellular automata (FCA) are continuous cellular automata where the local rule is defined as the “fuzzification" of the local rule of a corresponding Boolean cellular automaton in disjunctive normal form. In this paper, we are interested in the relationship between Boolean and fuzzy models and, for the first time, we analytically show the existence of a strong connection between them by fo...

In this paper, a classification method based on fuzzy linguistic rules is exposed. It is applied for the recognition of the gradual color of wood in an industrial context. The wood, which is a natural material, implies uncertainty in the definition of its color. Moreover, the timber context leads obtaining imprecise data. Several factors can have an impact on the sensors (ageing of the acquisit...

Inclusion for fuzzy sets was &rst introduced by Zadeh in his seminal 1965 paper. Since it was found that the de&nition of inclusion was not in the true spirit of fuzzy logic, various researchers have set out to de&ne alternative indicators of the inclusion of one fuzzy set into another. Among these alternatives, the indicator proposed by Sinha and Dougherty stands out as an intuitively appealin...

If-then rules are one of the most common forms of knowledge discovered by data mining methods. The number and the length of extracted rules tend to increase with the size of a database, making the rulesets less interpretable and useful. Existing methods of extracting fuzzy rules from numerical data improve the interpretability aspect, but the dimensionality of fuzzy rulesets remains high. In th...

We present a new information architecture based on one recently introduced to structure categorized but otherwise unstructured information. The new architecture is based on fuzzy set theory subset operations that define graph theory nodes. Two types of graph edges are defined such that a user interface based on this architecture can logically minimize the number of visible navigable edges and a...

Complexities of geological processes portrayed as certain feature in a map (e.g., faults) are natural sources of uncertainties in decision-making for exploration of mineral deposits. Besides natural sources of uncertainties, knowledge-driven (e.g., fuzzy logic) mineral prospectivity mapping (MPM) is also plagued and incurs further uncertainty in subjective judgment of analyst when there is no r...