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Approximate Solutions of Initial Value Problems for Ordinary Differential Equations Using Radial Basis Function Networks

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Abstract

We present a numerical approach for the approximate solutions of first order initial value problems (IVP) by using unsupervised radial basis function networks. The proposed unsupervised method is able to solve IVPs with high accuracy. In order to demonstrate the efficiency of the proposed approach, we also compare its solutions with the solutions obtained by a previously proposed neural network method for representative examples.

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Authors and Affiliations

  1. Department of Mathematics, Eastern Mediterranean University, Mersin 10, Famagusta, North Cyprus, Turkey

    Fatma B. Rizaner

  2. Department of Information Technology, Eastern Mediterranean University, Mersin 10, Famagusta, North Cyprus, Turkey

    Ahmet Rizaner

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  1. Fatma B. Rizaner
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  2. Ahmet Rizaner
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Correspondence to Fatma B. Rizaner.

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Rizaner, F.B., Rizaner, A. Approximate Solutions of Initial Value Problems for Ordinary Differential Equations Using Radial Basis Function Networks. Neural Process Lett 48, 1063–1071 (2018). https://doi.org/10.1007/s11063-017-9761-9

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  • DOI: https://doi.org/10.1007/s11063-017-9761-9

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