提出了一种用于分类的模糊基函数（FBF）神经网络在线跟踪自学习算法，通过带有遗忘因子的样本均值和样本协方差矩阵，保存了原始样本所包含的类可能性分布信息，并在此基础上产生新增样本的目标输出用于训练FBF网络，以实现分类边界的在线跟踪；给出了带有遗忘因子的样本均值和样本协方差矩阵的递推算法，以克服传统方法需要保存大量以往训练样本带来的困难。所提出的方法用于旋转机械的故障识别，结果表明是可行的和有效的。

An on-line tracking self-learning algorithm for fuzzy basis function (FBF) neural network classifier is proposed in this paper.  Based on the previous possibility distribution of the clusters,  which is kept within the sample mean and covariance matrix with forgetting factor,  a strategy for constructing the target output of the new training sample set is given.  With the new sample set the FBF network can be trained to track the variable clustering boundary.  Meanwhile,  a recursive algorithm for computing the sample mean and covariance matrix with forgetting factor is also proposed to overcome the difficult of storing the vast old training samples.  The proposed method is used for fault recognition of the rotating machinery,  and the results show that it is feasible and effective.