Extreme learning machine (ELM) is a fast learning mechanism used in many domains. Unsupervised ELM has improved to extract nonlinear features. A nonlinear dynamic process monitoring method named sparse representation preserving embedding based on ELM (SRPE-ELM) is proposed in this paper. First, the noise is removed by sparse representation and the sparse coefficient is applied to construct the adjacency graph. The adjacency graph with a data-adaptive neighborhood can extract dynamic manifold structure better than a specified neighborhood parameter. Secondly, a new objection function considered the sparse reconstruction and output weights is established to extract nonlinear dynamic manifold structure. Thirdly, the statistic SPE and T2 based on SRPE-ELM are built to monitor the whole process. Finally, SRPE-ELM is applied in the IRIS data classification example, a numerical case and Tennessee Eastman benchmark process to verify the effectiveness of process monitoring.

中文翻译：

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基于极限学习机的稀疏表示保留嵌入过程监控

极限学习机（ELM）是一种用于许多领域的快速学习机制。无监督 ELM 已改进以提取非线性特征。提出了一种基于ELM的稀疏表示保留嵌入非线性动态过程监控方法(SRPE-ELM)。首先，通过稀疏表示去除噪声，并应用稀疏系数来构建邻接图。具有数据自适应邻域的邻接图可以比指定的邻域参数更好地提取动态流形结构。其次，建立一个考虑稀疏重构和输出权重的新目标函数来提取非线性动态流形结构。第三，建立了基于SRPE-ELM的统计SPE和T2，对整个过程进行监控。最后，