Fuzzy models of language structures

Torralba, Francisco Criado; Gachechiladze, T.; Meladze, Hamlet; Tsertsvadze, G. Z.

doi:10.1109/tfuzz.2002.800655

Cited by 2 publications

(1 citation statement)

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“…Recent literature has explored such combination modeling by applying it to similarity-based clustering [7], fuzzy decision trees [8], and fuzzy quantitative models of language statistics [9]. This paper shows that such models may be applied to implement a novel method for an automatic selection of continuous wavelets that best represent time-series gait sequences.…”

Section: Introductionmentioning

confidence: 99%

Modeling Fuzziness Measures for Best Wavelet Selection

Arafat

Skubic

2008

IEEE Trans. Fuzzy Syst.

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Uncertainty measures model different types of uncertainty that are inherent in complex information systems. Measures that model either fuzzy or probabilistic uncertainty types have been explored in the literature. This paper shows that a combination of fuzzy and probabilistic uncertainty types, combined with the generalized maximum uncertainty principle, can be applied to time-series sequence classification and analysis. We present a novel algorithm that selects a wavelet from a wavelet library such that it best represents a time-series sequence, in a maximum uncertainty sense. Transformation coefficients are combined together in feature vectors that capture sequence temporal trends. A neural network is trained and tested using extracted gait sequence temporal features. Results have shown that models that combine together fuzzy and probabilistic uncertainty types better classify time-series gait sequences.

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Section: Introductionmentioning

confidence: 99%