Abstract
Computational Intelligence techniques are becoming more and more popular in the treatment of classical mathematical problems. The use of Neural Networks (NN) for the solution of Differential Equations is not a new perspective, as this scientific area is active for more than twenty five years. Nevertheless, there are many aspects that can be studied in both theoretical and technical point of view. In this work we investigate the NN based approximate solution of stiff Initial Value Problems (IVPs). We extend the work of Lagaris et al [1] and propose an alternative NN architecture and activation function choices. In order to reveal the capabilities of NN solutions we compare our solutions to the solutions of standard MATLAB stiff solvers.
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