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Optimized Fuzzy Hypersphere Neural Network with Online Adaptation Capability

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Applied Computational Technologies (ICCET 2022)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 303))

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Abstract

The Fuzzy min-max network (FMN), fuzzy hypersphere neural network classifier (FHSNN), and other FMN variants are sensitive to hyperbox/hypersphere (HB/HS) size or expansion parameter. The neural network of these models needs to be repeatedly trained to find the best value of expansion parameter for which network provides near about full classification accuracy with a minimum number of HBs/HSs. The user-defined expansion parameter ranges between 0 to 1. Hence these models scan input patterns multiple times for various values of the expansion parameter. In addition to it, its value needs to be retuned by retraining the existing patterns while adapting the new input patterns or classes.

The paper proposes an Optimized Fuzzy Hypersphere Neural Network Classifier with online adaptation capability (OFHSNNwOC). The designed algorithm trains the network in a single pass that means without scanning the input dataset multiple times. The modifications proposed in the model are eviction of the expansion parameter, calculation of centre points and radii of FHSs in an optimized way, membership functions for various kinds of hyperspheres, and online capability for adaptation of new input patterns or classes in the already built model with no need for retraining.

The classifier is assessed and examined utilizing the benchmark datasets.

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Authors and Affiliations

  1. Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, Maharastra, India

    M. S. Mahindrakar & U. V. Kulkarni

Authors
  1. M. S. Mahindrakar
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  2. U. V. Kulkarni
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Correspondence to M. S. Mahindrakar .

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Editors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Dr. B. A. Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  2. Department of Digital Education and Policy, Swansea University, Swansea, UK

    Tom Crick

  3. Creative Technologies and Product Design, National Taipei University of Business, Taipei City, Taiwan

    Sheng-Lung Peng

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Mahindrakar, M.S., Kulkarni, U.V. (2022). Optimized Fuzzy Hypersphere Neural Network with Online Adaptation Capability. In: Iyer, B., Crick, T., Peng, SL. (eds) Applied Computational Technologies. ICCET 2022. Smart Innovation, Systems and Technologies, vol 303. Springer, Singapore. https://doi.org/10.1007/978-981-19-2719-5_7

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