Abstract:

As the traditional partial least squares (PLS) method has the problem of tedious steps in the process modeling, it is difficult to obtain the online performance evaluation model of complex process.The autoregressive projection to latent structures (AR?PLS) algorithm was used to establish the predictive model for the related process data.An online performance evaluation model was established by the performance levels of the output data which was divided by the fuzzy C?means clustering algorithm. The simulation results show that compared with the traditional partial least squares method, the modeling process of this method is simpler and the computational complexity is reduced, it has certain application value.

Key words: Autoregressive projection to latent structures, Fuzzy C?Means clustering, Partial least squares, Performance evaluation

摘要：

传统偏最小二乘法(PLS)在工业过程建模中运算过程繁琐，复杂工业过程的性能评价模型难以在线获得。采用自回归潜结构投影(AR?PLS)算法对相关过程数据建立预测模型，通过模糊C均值聚类算法划分输出数据的性能等级，建立一种在线性能评价模型。仿真结果表明，该方法相比传统偏最小二乘法建模过程简便，计算复杂度低，具有一定的应用价值。

关键词: 自回归潜结构投影, 模糊C均值聚类, 偏最小二乘法, 性能评价