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    一种新颖的神经网络稳健估计方法

    A NOVEL ROBUST ESTIMATION METHOD FOR NEURAL NETWORK

    • 摘要: 当神经网络应用于实际工程问题时,网络的训练数据集或多或少都有噪声或异常值掺入其中.为了使网络具有更好的稳键性,文中根据稳健统计学原理,针对前馈神经网络(FNN)提出了一种稳健估计(RE)函数作为新的网络目标函数.以函数逼近问题为例,理论分析和计算机模拟实验证明了RE方法的优越性:既基本保持了最小二乘估计(LSE)方法的优点,又能在抵抗异常值(outliers)干扰方面优于LSE方法,使FNN更具实用性

       

      Abstract: When neural network (NN) is applied to practical engineering, there are more or less some noises or outliers in training data sets. To make NN hold more robust, a novel robust estimation function is proposed to serve as new target function of feedforward neural network (FNN) according to the theorem of robust statistics in the paper. The results of the theoretical analysis and simulation for function approximation problem show superiority of the method, which not only holds basically the merit of the least square estimate (LSE) method but also has robustness against outliers.

       

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