Townsend N W, Tarassenko L
Neural Networks Research Group, Department of Engineering Science, Oxford University, Oxford, OX1 3PJ, UK.
IEEE Trans Neural Netw. 1999;10(2):217-30. doi: 10.1109/72.750542.
Neural networks are being increasingly used for problems involving function approximation. However, a key limitation of neural methods is the lack of a measure of how much confidence can be placed in output estimates. In the last few years many authors have addressed this shortcoming from various angles, focusing primarily on predicting output bounds as a function of the trained network's characteristics, typically as defined by the Hessian matrix. In this paper the problem of the effect of errors or noise in the presented, input, vector is examined and a method based on perturbation analysis of determining output bounds based on both the error in the input vector and the imperfections in the weight values after training is presented and demonstrated.
神经网络正越来越多地用于涉及函数逼近的问题。然而，神经方法的一个关键限制是缺乏一种衡量对输出估计可信赖程度的方法。在过去几年中，许多作者从不同角度解决了这一缺点，主要集中于将输出界限预测为训练网络特征的函数，通常由海森矩阵定义。本文研究了呈现的输入向量中的误差或噪声的影响问题，并提出并演示了一种基于扰动分析的方法，该方法根据输入向量中的误差和训练后权重值的不完美来确定输出界限。
