System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused o...

-The traditional query in relational database is unable to satisfy the needs for dealing with fuzzy linguistic values. In this paper, a new data query technique composed of fuzzy theory and MS-SQL is provided. Here, the query can be implemented for fuzzy linguistic variables query via an interface to Microsoft ASP.NET. It is being applied to an realistic instance i.e. questions could be express...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

This paper presents an evolutionary Multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler and still accurate linguistic fuzzy models by learning fuzzy inference operators and applying rule selection. The Fuzzy Rule Based Systems obtained in this way, have a better trade-off between interpretability and accuracy in ling...

The aim of this paper is to present the Temporal Fuzzy Chains (TFCs) [3] to model the dynamic systems in a linguistic manner. TFCs make use of two different concepts: the traditional method to represent the dynamic systems named state vectors [6], and the linguistic variables [8] used in fuzzy logic [7]. Thus, TFCs are qualitative and represents the ”temporal zones” using linguistic states and ...

The aim of this paper is to present the Temporal Fuzzy Chains (TFCs) [3] to model the dynamic systems in a linguistic manner. TFCs make use of two different concepts: the traditional method to represent the dynamic systems named state vectors [6], and the linguistic variables [8] used in fuzzy logic [7]. Thus, TFCs are qualitative and represents the ”temporal zones” using linguistic states and ...

The aim of this paper is to present an algorithm to induce the Temporal Fuzzy Chains (TFCs) (J. Moreno, 2002b). TFCs are used to model the dynamic systems in a linguistic manner. TFCs make use of two different concepts: the traditional method to represent the dynamic systems named state vectors (Ogata, 1998), and the linguistic variables (Zadeh, 1975) used in fuzzy logic (Tanaka, 1998). Thus, T...

this paper presents the basic concepts of stability in fuzzy linguistic models. theauthors have proposed a criterion for bibo stability analysis of fuzzy linguistic modelsassociated to linear time invariant systems [25]-[28]. this paper presents the basic concepts ofstability in the general nonlinear and linear systems. this stability analysis method is verifiedusing a benchmark system analysis.

Content-based image retrieval is one of the main research directions in information retrieval and digital library. However, the low-level image features used in traditional content-based image systems lack of semantics meanings of users. In this paper, we propose a multi-layer image data model to combine the low-level image features and high-level linguistic meanings. We analyze and represent a...

-In this work we present the methods of creating the knowledge bases by using the theory of fuzzy systems as well as the probability and stochastic processes theory. We show that such knowledge-based systems can be applied in different diagnostic tasks. The structure of the reason-result fuzzy model is predefined by experts at the beginning of the task. The notion of probability of fuzzy events...