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Hybrid Intelligent Systems Based on Fuzzy Logic and Deep Learning

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Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11866))

Abstract

The purpose of this lecture is to establish the fundamental links between two important areas of artificial intelligence - fuzzy logic and deep learning. This approach will allow researchers in the field of fuzzy logic to develop application systems in the field of strong artificial intelligence, which are also of interest to specialists in the field of machine learning. The lecture also examines how neuro-fuzzy networks make it possible to establish a link between symbolic and connectionist schools of artificial intelligence. A lot of methods of rule extraction from neural networks are also investigated.

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Authors and Affiliations

  1. Federal Research Centre of Informatics and Computer Science of RAS, Moscow, Vavilova, 42, Moscow, Russia

    Alexey Averkin

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  1. Alexey Averkin
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Correspondence to Alexey Averkin .

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Editors and Affiliations

  1. Federal Research Center "Computer Science and Control", Moscow, Russia

    Gennady S. Osipov

  2. Federal Research Center "Computer Science and Control", Moscow, Russia

    Aleksandr I. Panov

  3. Federal Research Center "Computer Science and Control", Moscow, Russia

    Konstantin S. Yakovlev

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Averkin, A. (2019). Hybrid Intelligent Systems Based on Fuzzy Logic and Deep Learning. In: Osipov, G., Panov, A., Yakovlev, K. (eds) Artificial Intelligence. Lecture Notes in Computer Science(), vol 11866. Springer, Cham. https://doi.org/10.1007/978-3-030-33274-7_1

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  • DOI: https://doi.org/10.1007/978-3-030-33274-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33273-0

  • Online ISBN: 978-3-030-33274-7

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