Fuzzy regions: interpretations of surface area and distance
Fuzzy relation equation under a class of triangular norms: A survey and new results.
By substituting the classical lattice operator min of the unit real interval with a triangular norm of Schweizer and Sklar, the usual fuzzy relational equations theory of Sanchez can be generalized to wider theory of fuzzy equations. Considering a remarkable class of triangular norms, for such type of equations defined on finite sets, we characterize the upper an lower solutions.We also characterize the solutions posessing a minimal fuzziness measure of Yager valued with respect to a triangular...
Fuzzy relation equations under LSC and USC t-norms and their Boolean solutions.
This paper follows a companion paper (Stochastica 8 (1984), 99-145) in which we gave the state of the art of the theory of fuzzy relation equations under a special class of triangular norms. Here we continue this theory establishing new results under lower and upper semicontinuous triangular norms and surveying on the main theoretical results appeared in foregoing papers. Max-t fuzzy equations with Boolean solutions are recalled and studied. Many examples clarify the results established.
Fuzzy sets and fusion of multisensor data
Fuzzy sets as set classes.
Fuzzy sets have been studied in various forms. We now offer a presentation of fuzzy sets whereby they are conceived as representatives of a whole class of sets (that are themselves subsets of the universe of objects on which the fuzzy set is defined).
Fuzzy sets in pattern recognition, image analysis and automatic speech recognition
Fuzzy set theory, a recent generalization of classical set theory, has attracted the attention of researchers working in various areas including pattern recognition, which has had a seminal influence in the development of this new theory. This paper attempts to discuss some of the methodologies that have been suggested for pattern recognition, and techniques for image processing and speech recognition.
Fuzzy zero, algebraic equivalence: yes or no?
Guest Editorial: Fuzzy logic and soft computing in an effective and efficient processing of imperfect information - in memoriam Ashley Morris
How similarity matrices are?
In finite sets with n elements, every similarity relation (or fuzzy equivalence) can be represented by an n x n-matrix S = (sij), sij ∈ [0,1], such that sii = 1 (1 ≤ i ≤ n), sij = sji for any i,j and S o S = S, where o denotes the max-min product of matrices. These matrices represent also dendograms and sets of closed balls of a finite ultrametric space (vid. [1], [2], [3]).
Information in vague data sources
This paper deals with the concept of the “size“ or “extent“ of the information in the sense of measuring the improvement of our knowledge after obtaining a message. Standard approaches are based on the probabilistic parameters of the considered information source. Here we deal with situations when the unknown probabilities are subjectively or vaguely estimated. For the considered fuzzy quantities valued probabilities we introduce and discuss information theoretical concepts.
Information Measure for Vague Symbols
The structures of the fuzzy information theory are focused on the concept of fuzzy entropy, where the individual information of symbols is considered only implicitely. This paper aims to fill this gap and to study the concepts of fuzzy information. Special attention is paid to the typical fuzzy set theoretical paradigma of monotonicity of operations.
Information measures and uncertainty of particular symbols
The measurement of information emitted by sources with uncertainty of random type is known and investigated in many works. This paper aims to contribute to analogous treatment of information connected with messages from other uncertain sources, influenced by not only random but also some other types of uncertainty, namely with imprecision and vagueness. The main sections are devoted to the characterization and quantitative representation of such uncertainties and measures of information produced...
Information systems in categories of valued relations.
The paper presents a categorical version of the notion of information system due to D. Scott. The notion of information system is determined in the framework of ordered categories with involution and division and the category of information systems is constructed. The essential role in all definitions and constructions play correlations between inclusion relations and entailment relations.
Integral nets and fuzzy relations in deterministic automata
Interpretability of linguistic variables: a formal account
This contribution is concerned with the interpretability of fuzzy rule-based systems. While this property is widely considered to be a crucial one in fuzzy rule-based modeling, a more detailed formal investigation of what “interpretability” actually means is not available. So far, interpretability has most often been associated with rather heuristic assumptions about shape and mutual overlapping of fuzzy membership functions. In this paper, we attempt to approach this problem from a more general...
La energía informacional de orden α y tipo β como criterio de comparación de sistemas de información difusa.
En este trabajo se establece un criterio de comparación entre sistemas de información difusa basado en la maximización de la energía informacional esperada de orden α y tipo β y se comprueba que verifica las propiedades más relevantes que a nuestro juicio debe verificar un criterio de comparación.
Large variable systems of higher degrees in fuzzification process. Fuzzification of systems for technical and medical practice. II
Logical Aggregation Based on Interpolative Boolean Algebra.
Los sistemas de información difusos y la definición probabilística de Zadeh.
Mathematical aspects of the theory of measures of fuzziness.
After recalling the axiomatic concept of fuzziness measure, we define some fuzziness measures through Sugeno's and Choquet's integral. In particular, for the so-called homogeneous fuzziness measures we prove two representation theorems by means of the above integrals.