The dynamic acquisition of knowledge in machine learning inquiry in the function approximation

This paper puts dynamic acquisition of knowledge in machine learning into neural network approach, then study neural networks about the methods of function approximation, firstly analyzes some relevant theories about the neural networks in function approximation. And then putting BP neural networks into function approximation and getting the desired results through experiments.Finally, applied the GRNN (Generalized Regression neural networks) to the actual function approximation, getting the result of function approximation in which error has been minimal. And through experiment verify this neural network which has the advantage of raining high speed and strong non-linear mapping capability.