1,721,204 research outputs found

    Tensor based analysis of quantized chaotic pseudo-Markov processes

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    This contribution represents the slides of a tutorial held by the author

    FM-based generation of high EMC timing signals

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    This contribution represents the slides of a tutorial held by the author

    S-Norm aggregation of infinite collections

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    The aggregation of membership values by means of theiterate application of an s-norm is of cardinal relevance for thedevelopment of any general theory of approximate reasoning whichrelies on a {\em sup-t} model for relation composition. Here wepresent a definition of s-norm aggregation for possibly infinitecollections, which can be demonstrated to consistently generalizeprevious approaches. It provides a common framework in which classical{\em sup-t} and finite {\em s-t} compositions can be analyzed as wellas other significant cases. General properties of the s-normaggregation concept, its specialization for the class of continuouss-norms and its relationship with fuzzy measure theory are discusse

    On the Approximation Capabilities of the Homogeneous Takagi-Sugeno Model

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    The approximation capability of the Takagi-Sugeno model are investigated in this paper. This kind of model features a functional consequent which is usually a first-degree polynomial in the inputs. The importance of the constant term in such a polynomial is highlighted showing that if it is discarded the Takagi-Sugeno model features the same approximation power of the simpler constant-consequence systems

    Rule Reduction Algorithm for SISO Takagi-Sugeno Models

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    Rule Reduction Algorithm for SISO Takagi-Sugeno Model

    Q-norms and Generalized Relational Composition on Dense Universes

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    Q-norms and generalized relational composition on dense universe

    Fuzzy systems with overlapping Gaussian concepts: Approximation properties in Sobolev norms

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    In this paper the approximating capabilities of fuzzy systems with overlapping Gaussian concepts are considered. The target function is assumed to be sampled either on a regular gird or according to a uniform probability density. By exploiting a connection with Radial Basis Functions approximators, a new method for the computation of the system coefficients is provided, showing that it guarantees uniform approximation of the derivatives of the target function
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