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Interpretability of Fuzzy Systems Designed in the Process of Gradient Learning

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Design of Interpretable Fuzzy Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 684))

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Abstract

Supervised learning of fuzzy systems can be used to select their structure and parameters. There are few approaches to supervised learning and one of them is gradient learning (Sect. 2.2) . Impact on interpretability of fuzzy systems designed by gradient learning is a complex issue. It involves operations related to initialization, choice of a learning algorithm, realization of a learning process and also the final tuning of a system.

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  1. Institute of Computational Intelligence, Częstochowa University of Technology, Częstochowa, Poland

    Krzysztof Cpałka

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Cpałka, K. (2017). Interpretability of Fuzzy Systems Designed in the Process of Gradient Learning. In: Design of Interpretable Fuzzy Systems. Studies in Computational Intelligence, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-319-52881-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-52881-6_5

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