Effect of Object Function on the Performance of the Neural Network
Du Haifeng,Wang Sun'an
(School of Mechanical Engineering,Xi'an Jiaotong University,Xi'an 710049,China)

Abstract: The capability of the general object functions is introduced. The effect of the object function on the statistical performance and astringency of the neural network (NN) is analyzed. Based on Lyapinov theorem, the astringency of the NN is proved under the general object function. The approximate speed and the statistical performance are explored under the different object function. The theoretica analysis and experiments indicate that the astringency can be improved through the better selectivity object function,and the appropriate object function can guarantee that NN approximates to the function completely.
Keywords: object function;neural network;astringency