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Reasoning with Cognitive Structures of Agents I: Acquisition of Rules for Computational Theory of Perceptions by Fuzzy Relational Methods

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Fuzzy If-Then Rules in Computational Intelligence

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 553))

Abstract

Generalized fuzzy rules provide the essential means for computational representation of equational and inequality constraints over fuzzy granular structures. In this chapter, we are concerned with the ways the rules can be acquired from the real world data. We also provide a method for elicitation of knowledge of experts and link this to Zadeh’s Computational Theory of Perceptions. The algorithms for extracting and consistency checking of the rules explore the BK-products of relations. This makes it possible to treat the numerical and linguistic computations in a unified framework with the methods for extrapolation of knowledge and data.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, Florida State University, Tallahassee, Florida, 32306-4530, USA

    Ladislav J. Kohout & Eunjin Kim

Authors
  1. Ladislav J. Kohout
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  2. Eunjin Kim
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Editor information

Editors and Affiliations

  1. Belgian Nuclear Research Centre, SCK•CEN, Belgium

    Da Ruan

  2. University of Ghent, Belgium

    Etienne E. Kerre

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Kohout, L.J., Kim, E. (2000). Reasoning with Cognitive Structures of Agents I: Acquisition of Rules for Computational Theory of Perceptions by Fuzzy Relational Methods. In: Ruan, D., Kerre, E.E. (eds) Fuzzy If-Then Rules in Computational Intelligence. The Springer International Series in Engineering and Computer Science, vol 553. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4513-2_8

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  • DOI: https://doi.org/10.1007/978-1-4615-4513-2_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-7035-2

  • Online ISBN: 978-1-4615-4513-2

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