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Linguistic models and linguistic modeling.

W Pedryez1, A V Vasilakos

  • 1Dept. of Electr. & Comput. Eng., Alberta Univ., Edmonton, Alta.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 7, 2008
PubMed
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This study introduces linguistic models, which use data-driven labels instead of numbers for system modeling. This rapid prototyping approach offers a new way to understand complex data through associated linguistic granules.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Modeling

Background:

  • Traditional modeling relies heavily on numerical data and estimation.
  • A gap exists in models that interpret data using human-understandable labels.
  • Fuzzy (granular) models offer a potential alternative but often require complex numerical methods.

Purpose of the Study:

  • To propose a novel category of fuzzy (granular) models based on a linguistic approach.
  • To develop a method for designing models using meaningful linguistic labels (granules) from experimental data.
  • To present a rapid prototyping design mode for linguistic models, avoiding complex numerical estimation.

Main Methods:

  • Utilizing an augmented clustering technique (context-based clustering) centered on linguistic contexts.

Related Experiment Videos

  • Defining linguistic contexts as collections of fuzzy sets or fuzzy relations in the input variable space.
  • Constructing models as webs of associations between linguistic granules derived from data.

    • Main Results:

    • Demonstrated a method for creating linguistic models directly from data granules.
    • Successfully contrasted the proposed linguistic modeling approach with standard numerical methods.
    • Illustrated the utility of linguistic modeling through numerical studies with synthetic and real-world time-series data.

    Conclusions:

    • Linguistic models offer a viable and intuitive alternative to traditional numerical modeling techniques.
    • The context-based clustering algorithm enables the rapid design of effective linguistic models.
    • This approach is particularly useful for modeling complex systems like telecommunication traffic intensity.