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Training set fuzzification based on histogram to increase the performance of a neural network

Author

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  • Volna, Eva
  • Jarusek, Robert
  • Kotyrba, Martin
  • Zacek, Jaroslav

Abstract

This article describes a new approach which uses a histogram to fuzzify variables. We used a linguistic expression to form a training set output vector. The whole fuzzification process of the training set output vector is described in detail. This proposed method was verified on a real data set. We found out that the adaptation of a neural network by fuzzified output vectors has a considerably lower prediction error rate compared with another one without such transformation. Another advantage of the fuzzification approach is that only one neural network can be used for more various data sets with a high range of data attributes (units, thousands, millions). The proposed improvements increase the performance of neural networks, which is presented in the final part.

Suggested Citation

  • Volna, Eva & Jarusek, Robert & Kotyrba, Martin & Zacek, Jaroslav, 2021. "Training set fuzzification based on histogram to increase the performance of a neural network," Applied Mathematics and Computation, Elsevier, vol. 398(C).
    • Handle: RePEc:eee:apmaco:v:398:y:2021:i:c:s0096300321000424
    DOI: 10.1016/j.amc.2021.125994
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    References listed on IDEAS

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    1. Marek Šimon & Iveta Dirgová Luptáková & Ladislav Huraj & Marián Hosťovecký & Jiří Pospíchal, 2017. "Combined Heuristic Attack Strategy on Complex Networks," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, September.
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    Cited by:

    1. Zhou, Yihong & Zhang, Xiao & Ding, Feng, 2022. "Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models," Applied Mathematics and Computation, Elsevier, vol. 414(C).

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