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The solution of nonlinear Ordinary Differential Equations (ODE) finds potential applications in physics, economics, computing and engineering. Conventional approaches used for solving ODE are effective in case of 1st order or 2nd order problems. With increase in order the complexity associated with the problem increases. Thus instead of going for an exact solution, determining an approximate solution...
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...
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