摘要
本文根据统计学的稳健性原理,将柯西(Cauchy)函数作为新的神经网络目标函数。在网络参数相同的前提下,利用传统的均方目标函数和新的柯西目标函数对BP网络分别进行训练后,加入小噪声及异常值(Outliers)干扰对该网络进行测试。结果表明,具有稳健性目标函数的网络不但有更快的收敛速度,而且对异常值有更好的抵抗能力。
In this paper, the Cauchy function is taken as a new target function of neural network accordings to the robustness theorem of statistics. Under the same network parameter conditions the BP net is trained using both mean squares and Cauchy target function first, then the net is tested by data sets including small Gaussian noises and outliers separately. Simulation results indicate that the network has both faster convergence speed and better performance against outliers after learning with robust target function.
基金
国家教委博士点基金
重庆市科委资助课题
