It is introduced an approach to the problem of "building logics" over sets of facts with only structured by a fuzzy relation of "consequence". Notions of "Truth" and "Falsehood" are introduced by means of two generalizations for classical Modus Ponens and Modus Tollens. Studying this concepts a theoretical base to explain the mode of reasoning in expert systems is obtained. By studying fuzzy tr...

The practice, common in fuzzy expert systems, to map linguistic data into fuzzy sets has some important reverse implications: given membership functions that represent different terms of a linguistic variable in one natural language , we can set out to search which of them best matches a given term in another language , on the basis of similarity assessments between individual terms. In other w...

optimization of machining parameters is very important and the main goal in every machining process. surface finishing prediction is a pre-requirement to establish a center for automatic machining operations. in this research, a neuro-fuzzy approach is used in order to model and predict the surface roughness in dry turning. this approach has both the learning capability of neural network and li...

Within the field of linguistic fuzzy modeling with fuzzy rule-based systems, the automatic derivation of the knowledge base from numerical data is an important task. In this contribution, we propose a new approach to automatically learn the whole knowledge base, combining two different strategies for rules derivation and fuzzy partitions definition, working cooperatively in order to obtain accu...

The aim of this contribution is to point out the importance of interpretability of fuzzy rule-based systems. We try to approach this key property axiomatically, as a starting point, restricting to interpretability of linguistic variables (in Zadeh’s sense). The proposed approach is underlined with an intuitive example. Finally, the benefits of considering interpretability in design and tuning o...

Many real-world decision making problems require multiple criteria which can be from different nature. This implies to define a heterogeneous context with different types of information that experts will use to provide their assessments. But sometimes, experts do not have enough knowledge or information to assess the criteria and they hesitate to express their assessments. Therefore, in this co...

The high complexity of socio-economic environments often makes it difficult for a single decision maker (DM) to consider all important aspects of decision problems. Therefore, a group decision making (GDM) process is often preferred by organizations. Moreover, during the decision process, DMs may have difficulties in the prioritization of alternatives. Linguistic interval fuzzy preference relat...

An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds strongest dependencies from numerical data. An application example illustrates the usef...

This paper discusses a general approach to qualitative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, a...

It is common that experts involved in complex decision real world problems use natural language for expressing their knowledge in uncertain frameworks. The language is inherent vague, hence probabilistic decision models are not very suitable in such cases. Therefore, other tools as fuzzy logic and fuzzy linguistic approach have been successfully used to model and manage such vagueness. The use ...