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**Fuzzy fractal dimensions and fuzzy modeling.**
*(English)*
Zbl 1046.93501

Summary: Fractal dimensions describe self-similarity (structural complexity) of various phenomena (such as e.g., temporal signals, images). Their determination (through box counting or a correlation method) is inherently associated with the use of information granules/sets. The intent of this study is to generalize the idea to the domain of fuzzy sets and reveal associations between the mechanisms of fractal analysis and granular computing (including fuzzy modeling). First, we introduce the concept itself and discuss the role of fuzzy sets as a vehicle for constructing fractal dimensions. Second, we propose an algorithmic framework necessary to carry out all computing, and experimentally quantify a performance of regression models used to determine fractal dimensions and contrast it with the performance of the fractal models existing in the set-based environment. It is shown that the fuzzy set approach produces more consistent models (in terms of their performance). We also postulate a power law of granularity and discuss its direct implications in the form of a variable granularity in fuzzy modeling. In particular, we show how the power law of granularity helps to construct mappings between systems’ variables in rule-based models. Experimental studies involving several frequently used categories of fuzzy sets illustrate the main features of the approach.

### MSC:

93A30 | Mathematical modelling of systems (MSC2010) |

28A80 | Fractals |

### Keywords:

Fractals; Self-similarity; Structural complexity; Fractal dimension; Generalized correlation fractal dimension; Power law of information granularity; Variable granularity in fuzzy modeling
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\textit{W. Pedrycz} and \textit{A. Bargiela}, Inf. Sci. 153, 199--216 (2003; Zbl 1046.93501)

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### References:

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This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.