An artificial neural networks for approximating polynomial functions

The authors use polynomial function as a common base to measure the capacity of a feedforward artificial neural network (FANN) with a finite number of hidden nodes. They show that there is a relationship between the capacity of a FANN in approximating polynomial functions and the number of hidden nodes used in the FANN. A procedure for realizing a FANN in approximating polynomial functions is described. Two examples are given to show the procedure. Several experiments are reported, verifying that a FANN with a certain number of hidden nodes has the capability to learn a given polynomial function. The experiments also showed that the proposed algorithm for training a FANN performs accurately