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A Review on the Hybridization of Fuzzy Systems and Machine Learning Techniques

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Computer Vision and Robotics

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

Fuzzy systems are used in modeling and implementation of many real-world applications operating under an imprecise and uncertain environment. Such systems have effective learning and reasoning capabilities. By integration ML techniques, the fuzzy systems can work with better performance. The paper is a review on the application of the different types of machine learning techniques into fuzzy systems. Many research outcomes are extracted in the hybridization zone of the fuzzy systems and machine learning. The advantages and future scope are also discussed on the mutual integration of fuzzy systems and machine learning techniques.

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Authors and Affiliations

  1. Artificial Intelligence Research Center, Department of Computer Science & Engineering, School of Engineering, Babu Banarasi Das University, Lucknow, India

    Rajan Prasad & Praveen Kumar Shukla

Authors
  1. Rajan Prasad
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  2. Praveen Kumar Shukla
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Editors and Affiliations

  1. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  2. Department of Industrial Engineering and Computer Science, Stellenbosch University, Stellenbosch, South Africa

    Andries Engelbrecht

  3. Babu Banarasi Das University, Lucknow, Uttar Pradesh, India

    Praveen Kumar Shukla

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Prasad, R., Shukla, P.K. (2022). A Review on the Hybridization of Fuzzy Systems and Machine Learning Techniques. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_32

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