Interpolation results in the split inversion neural network algorithm

It is shown that the split inversion neural network algorithm can provide accurate interpolation for nonlinear situations that would be difficult to obtain with a look-up table. Neural networks trained with the algorithm are shown to provide complex interpolation when used as associative memories. The nonlinear relations need never be known; the network learns these from training with representative examples. The discretization of the parameters used in obtaining training samples and the nature of the underlying equations determine the accuracy of the interpolation. This approach has been shown to be valuable for engineering design where expertise is often dependent on association with previous designs that on following a set of rules. The neural network has been applied to the design of a beam-truss-spring structure