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International Conference on Intelligent Information Technologies for Industry

IITI 2017: Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17) pp 26–33Cite as

Cognitive Generator to Interpret Fuzzy Values

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 679))

Abstract

The game approaches are rather popular in many applications, where a collective of automata is used. In the present paper the game involves a group of learning finite automata. The game is played sequentially with one automaton at a time, the result of the game defines next automaton to be played with. The goal of this game to provide some measuring system that is a reminiscent of collecting statistics in Probability Theory but in a different manner.

For measuring of an unknown membership value a new concept has been introduced called Cognitive Generator which transforms a fuzzy singleton to ordinary crisp logic value. Considerations on various types of axiomatic approaches show that the Cognitive Generator, as well as our Evidence Combination Axiomatic, belongs to one class of axiomatic theories, which may be used in applications directly.

Some programming examples aimed to illustrate our general approach.

This work was supported by the Russian Fond for Basic Research, Grant 15-07-07486, and by the Program 1.5P of the Presidium of Russian Academy of Sciences.

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Notes

  1. 1.

    Such Pseudo Random Numbers Generators are used in the most of computer languages to imitate probability values. Below we will use for this purpose the function random from LISP.

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Author information

Authors and Affiliations

  1. Institute for Information, Transmission Problems of RAS, Moscow, Russian Federation

    Vadim L. Stefanuk

  2. RUDN University, Moscow, Russian Federation

    Vadim L. Stefanuk

Authors
  1. Vadim L. Stefanuk
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Correspondence to Vadim L. Stefanuk .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR labs), Auburn, Washington, USA

    Ajith Abraham

  2. Rostov State Transport University , Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University , Moscow, Russia

    Valery Tarassov

  4. VSB-Technical University of Ostrava , Ostrava, Czech Republic

    Vaclav Snasel

  5. Department Electrical Power Engineering, Technical University of Varna, Varna, Bulgaria

    Margreta Vasileva

  6. Rostov State Transport University , Rostov-on-Don, Russia

    Andrey Sukhanov

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Stefanuk, V.L. (2018). Cognitive Generator to Interpret Fuzzy Values. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds) Proceedings of the Second International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’17). IITI 2017. Advances in Intelligent Systems and Computing, vol 679. Springer, Cham. https://doi.org/10.1007/978-3-319-68321-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-68321-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68320-1

  • Online ISBN: 978-3-319-68321-8

  • eBook Packages: EngineeringEngineering (R0)

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