A new heuristic approach is proposed for the automated optimization of neural network.It combines fast optimal brain surgeon

dynamic optimization of learning parameters and fast cross-validation.The fast optimal brain surgeon optimizes architecture of neural network automatically.The dynamic optimization of learning parameters is able to dynamically vary its learning parameters and select optimal learning parameters.The fast cross-validation provides improvements of avoiding overfitting problem effectively.Experimental results demonstrate that with the heuristic approach

considerable performance gains are obtained compared to the other algorithms.This includes better generalization

optimal network architecture

and faster learning.