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Complex-Valued Multivariate Neural Network (MNN) Approximation by Parameterized Half-Hyperbolic Tangent Function

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  • Seda Karateke

    (Department of Software Engineering, Faculty of Engineering and Natural Sciences, Istanbul Atlas University, 34408 Istanbul, Türkiye)

Abstract

This paper deals with a family of normalized multivariate neural network (MNN) operators of complex-valued continuous functions for a multivariate context on a box of R N ¯ , N ¯ ∈ N . Moreover, we consider the case of approximation employing iterated MNN operators. In addition, pointwise and uniform convergence results are obtained in Banach spaces thanks to the multivariate versions of trigonometric and hyperbolic-type Taylor formulae on the corresponding feed-forward neural networks (FNNs) based on one or more hidden layers.

Suggested Citation

  • Seda Karateke, 2025. "Complex-Valued Multivariate Neural Network (MNN) Approximation by Parameterized Half-Hyperbolic Tangent Function," Mathematics, MDPI, vol. 13(3), pages 1-27, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:453-:d:1579833
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    References listed on IDEAS

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    1. Khudhayr A. Rashedi & Mohd Tahir Ismail & Sadam Al Wadi & Abdeslam Serroukh & Tariq S. Alshammari & Jamil J. Jaber, 2024. "Multi-Layer Perceptron-Based Classification with Application to Outlier Detection in Saudi Arabia Stock Returns," JRFM, MDPI, vol. 17(2), pages 1-13, February.
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