基于限定样本序贯极端学习机的模拟电路在线故障诊断

为解决故障特征样本分批加入时分类模型的在线更新问题，提出一种限定样本序贯极端学习机(LSSELM)。 LSSELM通过逐步添加新样本，同时剔除与其相似度最高的同类别旧样本来提高模型的动态适应能力，并通过Sherman-Morrison矩阵求逆引理来降低计算复杂度，实现输出权值的递推求解，完成模型的在线训练。将LSSELM用于模拟电路在线故障诊断，结果表明相比在线序贯极端学习机(OS-ELM)和LSSELM的诊断准确率更高，具有更好的泛化性能。

Online fault diagnosis of analog circuit based on limited-samples sequence extreme learning machine

To solve the problem of on-line updating for the classification model in the case of fault feature samples added in batches, a dynamic sequence extreme learning machine(LSSELM) is proposed. Dynamic adaptability of LSSELM is improved by adding a new feature sample, and meanwhile, abandoning an old one with the highest similar degree of the same label to it iteratively. Then, the Sherman-Morrison formula is used to decrease the calculation complexity, and the output weights are solved recursively, and the online training of classification model is completed. Finally, applying LSSELM to online fault diagnosis of the analog circuit, the simulation results show that the LSSELM can get higher diagnosis accurate and better generalization than the online sequence extreme learning machine(OS-ELM).