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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2022: Intelligent Systems and Applications pp 17–36Cite as

Robust Rule Based Neural Network Using Arithmetic Fuzzy Inference System

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 542))

Abstract

Deep Neural Networks (DNNs) are currently one of the most important research areas of Artificial Intelligence (AI). Various type of DNNs have been proposed to solve practical problems in various fields. However the performance of all these types of DNNs degrades in the presence of feature noise. Expert systems are also a key area of AI that are based on rules. In this work we wish to combine the advantages of these two areas. Here, we present Rule-Based Neural Networks (RBNNs) where each neuron is a Fuzzy Inference System (FIS). RBNN can be trained to learn various regression and classification tasks. It has relatively a few trainable parameters. It is robust to (input) feature noise and it provides a good prediction accuracy even in the presence of large feature noise. The learning capacity of the RBNN can be enhanced by increasing the number of neurons, number of rules and number of hidden layers. The effectiveness of RBNNs is demonstrated by learning real world regression and classification tasks.

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Acknowledgment

The study was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program. The research was also funded from the National Research, Development and Innovation Fund of Ministry of Innovation and Technology of Hungary under the TKP2021-NVA (Project no. TKP2021-NVA-09) funding scheme.

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Authors and Affiliations

  1. Institute of Informatics, University of Szeged, Szeged, 6720, Hungary

    József Dombi & Abrar Hussain

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  1. József Dombi
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  2. Abrar Hussain
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Correspondence to Abrar Hussain .

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Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

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Dombi, J., Hussain, A. (2023). Robust Rule Based Neural Network Using Arithmetic Fuzzy Inference System. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_2

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