Polymorphic fuzzy signatures

Abstract

The fuzzy signature [1], [2] approach is aimed at finding a hierarchically decomposed solutions by adding new elements to Zadeh's approach [3]. It tackles the problem by splitting the problem into hierarchically organized local sub-models and by applying more complex and heterogenous descriptors, more fit for the identification of extremely complex models. However, the computational time complexity still affects the fuzzy signatures as we were attempt to create an atomic fuzzy signature for each data point we get. Importantly, the atomic fuzzy signatures we store has properties we can make use of to make search over this structure computationally efficient. In this paper we introduce a new approach that uses the metadata about a set of fuzzy signatures to extract a Polymorphic Fuzzy Signature. Productively, a polymorphic fuzzy signature represents its base set of fuzzy signatures in a higher meta level which also allows search/inference, and so can reduce the computational time complexity of the inference process.

Multi-level Fuzzy Rules-Based Analysis of Virtual Team Performance

“…The Fuzzy signatures can reduce complexity with slightly more complicated aggregation techniques in huge fuzzy structures. Fuzzy signature can be considered as a special kind of multi-dimensional fuzzy data, where some of the dimensions are formed as a variable sub-group and determines features of a higher level parameter or group [12]. This means that instead of assigning a single membership grade to each element X, as it is done when defining original fuzzy sets, a set of quantitative features are assigned to each element X which can have a structure of another nested vector, this way providing additional information about that specific element of the domain.…”