Juan Martínez-Moreno

Meir-Keeler Type Multidimensional Fixed Point Theorems in Partially Ordered Metric Spaces

We study the existence and uniqueness of a fixed point of the multidimensional operators which satisfy Meir-Keeler type contraction condition. Our results extend, improve, and generalize the results mentioned above and the recent results on these topics in the literature.

Coincidence point theorems on metric spaces via simulation functions

Due to its possible applications, Fixed Point Theory has become one of the most useful branches of Nonlinear Analysis. In a very recent paper, Khojasteh et?al.?introduced the notion of simulation function in order to express different contractivity conditions in a unified way, and they obtained some fixed point results. In this paper, we slightly modify their notion of simulation function and we investigate the existence and uniqueness of coincidence points of two nonlinear operators using this kind of control functions.

Multidimensional Fixed-Point Theorems in Partially Ordered Complete Partial Metric Spaces under ()-Contractivity Conditions

We study the existence and uniqueness of coincidence point for nonlinear mappings of any number of arguments under a weak ( )-contractivity condition in partial metric spaces. The results we obtain generalize, extend, and unify several classical and very recent related results in the literature in metric spaces (see Aydi et al. (2011), Berinde and Borcut (2011), Gnana Bhaskar and Lakshmikantham (2006), Berzig and Samet (2012), Borcut and Berinde (2012), Choudhury et al. (2011), Karapinar and Luong (2012), Lakshmikantham and Ciric (2009), Luong and Thuan (2011), and Roldan et al. (2012)) and in partial metric spaces (see Shatanawi et al. (2012)).

Multidimensional coincidence point results for compatible mappings in partially ordered fuzzy metric spaces

In recent times, coupled, tripled and quadruple fixed point theorems have been intensively studied by many authors in the context of partially ordered complete metric spaces using different contractivity conditions. Roldan et al. showed a unified version of these results for nonlinear mappings in any number of variables (which were not necessarily permuted or ordered) introducing the notion of multidimensional coincidence point. Very recently, Choudhury et al. proved coupled coincidence point results in the context of fuzzy metric spaces in the sense of George and Veeramani. In this paper, using the idea of coincidence point for nonlinear mappings in any number of variables, we study a fuzzy contractivity condition to ensure the existence of coincidence points in the framework of fuzzy metric spaces provided with Hadzic type t-norms. Then, we present an illustrative example in which our methodology leads to the existence of coincidence points but previous theorems cannot be applied.

F-closed sets and coupled fixed point theorems without the mixed monotone property

In this paper we present the notion of F-closed set (which is weaker than the concept of F-invariant set introduced in Samet and Vetro (Ann. Funct. Anal. 1:46-56, 2010), and we prove some coupled fixed point theorems without the condition of mixed monotone property. Furthermore, we interpret the transitive property as a partial preorder and, then, some results in that paper and in Sintunavarat et al. (Fixed Point Theory Appl. 2012:170, 2012) can be reduced to the unidimensional case.MSC:46T99, 47H10, 47H09, 54H25.

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

Least-squares technique is well-known and widely used to determine the coefficients of a explanatory model from observations based on a concept of distance. Traditionally, the observations consist of pairs of numeric values. However, in many real-life problems, the independent or explanatory variable can be observed precisely (for instance, the time) and the dependent or response variable is usually described by approximate values, such as “about

Estimation of a Fuzzy Regression Model Using Fuzzy Distances

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Some applications of the study of the image of a fuzzy number

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