关键词: 鲁棒性, 径向基函数（RBF）, RBF神经网络, 模糊子空间聚类, &epsilon, -不敏感损失函数

Abstract: When training data in the noisy environment, the generalization performance of traditional RBF (radial basis function) neural network is degraded because of the deficiency of feature extraction mechanism. This paper proposes a novel modeling method, i.e., RBF neural network modeling using fuzzy subspace clustering (FSC-RBF-NN) which adds feature extraction mechanism to overcome this challenge. Compared with traditional RBF neural network modeling, the proposed method can extract different subspace features for different nodes in hidden layer according to the subspace features of FSC (fuzzy subspace clustering) method and the feature extraction mechanism. When the training data contain lots of noise features, the proposed method can still keep good generalization performance by using the feature extraction mechanism to remove noise features. The experimental results on the synthetic and real-
world datasets prove that the FSC-RBF-NN method has strong robustness in the noisy environment.

Key words: robustness, radial basis function (RBF), RBF neural network, fuzzy subspace clustering (FSC), ε-insensitive loss function