Fuzzy methods in remote sensing have received growing interest for their particular value in situations where the geographical phenomena are inherently fuzzy. A fuzzy approach is investigated for the classi® cation of sub-urban land cover from remote sensing imagery and the evaluation of classi® cation accuracy. Under the fuzzy strategy, fuzziness, intrinsic to both remotely sensed data and gro...

In this paper, we introduce a novel fuzzy method which is combined fuzzy relation, interaction probability values and hub structure to detect sub-communities in complex networks. We apply our method on yeast proteinprotein interaction network to identify the protein complexes. Compared with traditional method, more protein complexes have been identified by this new fuzzy method. Meanwhile, we e...

Modeling and solving real world problems is one of the most important issues in optimization problems. In this paper, we present an approach to solve Fuzzy Interval Flexible Linear Programming (FIFLP) problems that simultaneously have the interval ambiguity in the matrix of coefficients .In the first step, using the interval problem solving techniques; we transform the fuzzy interval flexible p...

In this paper, we introduce the notion of an action $Y_X$as a generalization of the notion of a module,and the notion of a norm $vt: Y_Xto F$, where $F$ is a field and $vartriangle(xy)vartriangle(y') =$ $ vartriangle(y)vartriangle(xy')$ as well as the notion of fuzzy norm, where $vt: Y_Xto [0, 1]subseteq {bf R}$, with $bf R$  the set of all real numbers. A great many standard mappings on algebr...

We introduce a new method for modeling rating (utility) functions which employs techniques from fuzzy set theory. The main idea is to build a hierarchical model, called a fuzzy operator tree (FOT), by recursively decomposing a rating criterion into sub-criteria, and to combine the evaluations of these sub-criteria by means of suitable aggregation operators. Apart from the model conception itsel...

In this report, we discuss results of modelling and forecasting nonstationary financial time series using a combination of the maximal overlap discreet wavelet transform (MODWT) and fuzzy logic. A financial time series is decomposed into an over complete, shift invariant wavelet representation. A fuzzy-rule base is created for each individual wavelet sub-series to predict future values. To form...