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Unbounded Fuzzy Hypersphere Neural Network Classifier

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Abstract

This paper presents the supervised classifier called unbounded fuzzy hypersphere neural network (UFHSNN) model. The basic fuzzy min-max neural network (FMMN), fuzzy hypersphere neural network (FHSNN) and many more its variants use expansion parameter to tune the model. Such tuned model gives good classification accuracy with minimum number of hyperboxes, but always needs to take multiple iterations to find optimal value of expansion parameter and train the model. However, in the proposed model, use of expansion parameter is removed, due to which the model can be trained in a single iteration. The different datasets are applied for verification of the model; also, outcomes are compared with some current FMMN variations. The analysis of outcomes shows that the presented model gives magnificent accuracy with reduced computational complexity.

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The authors did not receive support from any organization for the submitted work as well no funding was received to assist with the preparation of this manuscript.

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  1. SGGS Institute of Engineering and Technology, Vishnupuri, Nanded, Maharashtra, India

    M. S. Mahindrakar & U. V. Kulkarni

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  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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Mahindrakar, M.S., Kulkarni, U.V. Unbounded Fuzzy Hypersphere Neural Network Classifier. J. Inst. Eng. India Ser. B 103, 1335–1343 (2022). https://doi.org/10.1007/s40031-022-00719-5

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  • DOI: https://doi.org/10.1007/s40031-022-00719-5

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