Function approximation using backpropagation and general regression neural networks

The approximation capabilities of backpropagation (BP) neural networks and D. Specht's (1991) general regression neural network (GRNN) are compared using data generated from 14 functions under three levels of random noise. The results show that the BP approach provides significantly more accurate estimates than the GRNN approach, especially when the level of random noise in the data is low.<
>