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Throughout this paper, our main idea is to analyze the concepts of a strong negation and a restricted equivalence function, that appear in a natural way when dealing with theory and applications of fuzzy sets and fuzzy logic. Here we will use an analytical and topological approach, showing how to construct them in an easy way. In particular, we will also analyze some classical functional equation related to those key concepts. [ABSTRACT FROM AUTHOR]

In this paper we provide a specific construction method of ordered directionally monotone functions. We show that the functions obtained with this construction method can be used to build edge detectors for grayscale images. We compare the results of these detectors to those obtained with some other ones that are widely used in the literature. Finally, we show how a consensus edge detector can be built improving the results obtained both by our proposal and by those in the literature when applied individually. [ABSTRACT FROM AUTHOR]

The paper introduces a new class of functions from 0 , 1 n to 0 , n called d -Choquet integrals. These functions are a generalization of the "standard" Choquet integral obtained by replacing the difference in the definition of the usual Choquet integral by a dissimilarity function. In particular, the class of all d -Choquet integrals encompasses the class of all "standard" Choquet integrals but the use of dissimilarities provides higher flexibility and generality. We show that some d -Choquet integrals are aggregation/pre-aggregation/averaging/functions and some of them are not. The conditions under which this happens are stated and other properties of the d -Choquet integrals are studied. [ABSTRACT FROM AUTHOR]

In this work we study a new class of similarity measures between interval-valued fuzzy sets. The novelty of our approach lays, firstly, on the fact that we develop all the notions with respect to total orders of intervals; and secondly, on that we consider the width of intervals so that the uncertainty of the output is strongly related to the uncertainty of the input. For constructing the new interval-valued similarity, interval valued aggregation functions and interval-valued restricted equivalence functions which take into account the width of the intervals are needed, so we firstly study these functions, both in line with the two above stated features. Finally, we provide an illustrative example which makes use of an interval-valued similarity measure in stereo image matching and we show that the results obtained with the proposed interval-valued similarity measures improve numerically (according to the most widely used measures in the literature) the results obtained with interval valued similarity measures which do not consider the width of the intervals. [ABSTRACT FROM AUTHOR]

In this work we consider some classes of functions with relaxed monotonicity conditions generalizing some other given classes of fusion functions. In particular, directionally increasing aggregation functions (called also pre-aggregation functions), directionally increasing conjunctors, or directionally increasing implications, etc., generalize the standard classes of aggregation functions, conjunctors, or implication functions, respectively. We analyze different properties of these classes of functions and we discuss a construction method in terms of linear combinations of t-norms. [ABSTRACT FROM AUTHOR]

In this work, we introduce a method for constructing capacities using overlap indexes between the fuzzy sets which are generated from the inputs of the considered problem. We also use these capacities to aggregate information by means of the Choquet integral in a fuzzy rule-based classifier. We observe that with these capacities the obtained results are better than those obtained with other measures. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we study under which conditions overlap and grouping functions satisfy some commonly demanded properties such as migrativity or homogeneity. We also recall that the convex combination of overlap (grouping) functions is a new overlap (grouping) function. This property allows us to achieve a consensus between different methods that solve certain problems by means of these functions. We also show one application of this property in image processing. [Copyright &y& Elsevier]

Abstract: The problem of choosing an appropriate total order is crucial for many applications that make use of extensions of fuzzy sets. In this work we introduce the concept of an admissible order as a total order that extends the usual partial order between intervals. We propose a method to build these admissible orders in terms of two aggregation functions and we prove that some of the most used examples of total orders that appear in the literature are specific cases of our construction. [Copyright &y& Elsevier]

Abstract: We present the concept of strong equality index, basing ourselves on the definition of strong inclusion given by Dubois and Prade in 1980. We develop different construction methods using implication operators that satisfy two additional properties. We make an exhaustive study of these two properties. We also show another two construction theorems for strong equality indexes, one of them making use of order automorphisms and the other using the parametric families of t-norms, t-conorms and strong negations of Hamacher. We finish with an analysis of different relationships between fuzzy entropies and strong equality indexes. [Copyright &y& Elsevier]

Abstract: In this paper, we define the concept of an ignorance function and use it to determine the best threshold with which to binarize an image. We introduce a method to construct such functions from t-norms and automorphisms. By means of these new measures, we represent the degree of ignorance of the expert when given one fuzzy set to represent the background and another to represent the object. From this ignorance degree, we assign interval-valued fuzzy sets to the image in such a way that the best threshold is given by the interval-valued fuzzy set with the lowest associated ignorance. We prove that the proposed method provides better thresholds than the fuzzy classical methods when applied to transrectal prostate ultrasound images. The experimental results on ultrasound and natural images also allow us to determine the best choice of the function to represent the ignorance. [Copyright &y& Elsevier]

Abstract: In this paper we present a method to construct interval-valued fuzzy sets (or interval type 2 fuzzy sets) from a matrix (or image), in such a way that we obtain the length of the interval representing the membership of any element to the new set from the differences between the values assigned to that element and its neighbors in the starting matrix. Using the concepts of interval-valued fuzzy t-norm, interval-valued fuzzy t-conorm and interval-valued fuzzy entropy, we are able to detect big enough jumps (edges) between the values of an element and its neighbors in the starting matrix. We also prove that the unique t-representable interval-valued fuzzy t-norms and the unique s-representable interval-valued fuzzy t-conorms that preserve the length zero of the intervals are the ones generated by means of the t-norm minimum and the t-conorm maximum. [Copyright &y& Elsevier]

Abstract: In this paper we introduce a slight modification of the definition of migrativity for aggregation functions that allows useful characterization of this property. Among other things, in this context we prove that there are no t-conorms, uninorms or nullnorms that satisfy migrativity (with the product being the only migrative t-norm, as already shown by other authors) and that the only migrative idempotent aggregation function is the geometric mean. The k-Lipschitz migrative aggregation functions are also characterized and the product is shown to be the only 1-Lipschitz migrative aggregation function. Similarly, it is the only associative migrative aggregation function possessing a neutral element. Finally, the associativity and bisymmetry of migrative aggregation functions are discussed. [Copyright &y& Elsevier]

Abstract: In this paper we consider aggregation operators satisfying non-decreasingness and some specific boundary conditions. We then analyze some properties of such a family of aggregation operators, introducing the semiautoduality condition, which is weaker than the standard autoduality condition (i.e., the standard self De Morgan identity). Particular families of aggregation operators will appear depending on the context. [Copyright &y& Elsevier]

In this paper we apply restricted equivalence functions to the computation of the threshold of an image. In the first part we present an algorithm for obtaining the best threshold of a grayscale image with a single object. In the second part we study different algorithms for calculating the optimal threshold. Then we analyze two algorithms for obtaining a sequence of optimal thresholds in images with several objects. Lastly, we compare our results with those obtained with other methods and carry out a study of the time efficiency of the methods we propose. [Copyright &y& Elsevier]

Abstract: In this paper we present the concept of a restricted equivalence function. This concept arises on the one hand, from the definition of equivalence given by J. Fodor and M. Roubens, and on the other, from the properties usually demanded from the measures used for comparing images. We also study different methods for the construction of restricted equivalence functions from automorphisms and implication operators. Finally we analyze the manner of generating similarity measures of X. Liu and of J. Fan et al. from our restricted equivalence functions. [Copyright &y& Elsevier]

In this paper we study the conditions in which the implication operators satisfy the property I(x,c(x))=c(x) for all x∈[0,1], c being any strong negation. This study has led us to present different implication operator characterization theorems from automorphisms, obtaining a theorem similar to the one presented by Smets and Magrez (Internat. J. Approx. Reason. 1 (1987) 327), in which the strong negation c used is not generated by the same automorphism that generates the implication. [Copyright &y& Elsevier]

Intervals are a popular way to represent the uncertainty related to data, in which we express the vagueness of each observation as the width of the interval. However, when using intervals for this purpose, we need to use the appropriate set of mathematical tools to work with. This can be problematic due to the scarcity and complexity of interval-valued functions in comparison with the numerical ones. In this work, we propose to extend a generalization of the Sugeno integral to work with interval-valued data. Then, we use this integral to aggregate interval-valued data in two different settings: first, we study the use of intervals in a brain-computer interface; secondly, we study how to construct interval-valued relationships in a social network, and how to aggregate their information. Our results show that interval-valued data can effectively model some of the uncertainty and coalitions of the data in both cases. For the case of brain-computer interface, we found that our results surpassed the results of other interval-valued functions. [ABSTRACT FROM AUTHOR]

In this work we present an image reduction algorithm based on averaging aggregation functions. We axiomatically define the concepts of image reduction operator and local reduction operator. We study the construction of the latter by means of averaging functions and we propose an image reduction algorithm (image reduction operator). We analyze the properties of several averaging functions and their effect on the image reduction algorithm. Finally, we present experimental results where we apply our algorithm in two different applications, analyzing the best operators for each concrete application. [ABSTRACT FROM AUTHOR]

In this paper we stress the relevance of a particular family of fuzzy sets, where each element can be viewed as the result of a classification problem. In particular, we assume that fuzzy sets are defined from a well-defined universe of objects into a valuation space where a particular graph is being defined, in such a way that each element of the considered universe has a degree of membership with respect to each state in the valuation space. The associated graph defines the structure of such a valuation space, where an ignorance state represents the beginning of a necessary learning procedure. Hence, every single state needs a positive definition, and possible queries are limited by such an associated graph. We then allocate this family of fuzzy sets with respect to other relevant families of fuzzy sets, and in particular with respect to Atanassov''s intuitionistic fuzzy sets. We postulate that introducing this graph allows a natural explanation of the different visions underlying Atanassov''s model and interval valued fuzzy sets, despite both models have been proven equivalent when such a structure in the valuation space is not assumed. [Copyright &y& Elsevier]