论文标题
基于随机模糊数的回归的证据神经网络模型
An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
论文作者
论文摘要
我们引入了一个基于距离的神经网络模型，以进行回归，其中预测不确定性通过实际线路上的信念函数量化。该模型将输入矢量与原型的距离解释为以高斯随机模糊数（GRFN）表示的证据，并由广义产品交叉路口规则组合，这是一种将Dempster规则扩展到随机模糊集的操作员。网络输出是一个GRFN，可以通过三个数字来概括，这些数字表征了最合理的预测值，该值周围的可变性以及认知不确定性。与最先进的证据和统计学习算法相比，使用真实数据集的实验证明了该方法的表现非常好。 \关键字{证据理论，Dempster-Shafer理论，信念功能，机器学习，随机模糊集。
We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real line. The model interprets the distances of the input vector to prototypes as pieces of evidence represented by Gaussian random fuzzy numbers (GRFN's) and combined by the generalized product intersection rule, an operator that extends Dempster's rule to random fuzzy sets. The network output is a GRFN that can be summarized by three numbers characterizing the most plausible predicted value, variability around this value, and epistemic uncertainty. Experiments with real datasets demonstrate the very good performance of the method as compared to state-of-the-art evidential and statistical learning algorithms. \keywords{Evidence theory, Dempster-Shafer theory, belief functions, machine learning, random fuzzy sets.
