Nonlinear System Identification with Selective Recursive Gaussian Process Models

作者：Chan, Lester Lik Teck; Liu, Yi; Chen, Junghui^*

来源：Industrial & Engineering Chemistry Research, 2013, 52(51): 18276-18286.

DOI：10.1021/ie4031538

• GPR

The Gaussian process (GP) model has been applied to the identification of a process model. The GP model can be represented by its mean and covariance function. It provides predictive variance to the predictive distribution of the output and estimate of the variance of its predicted output. The GP model based method has shown to be successful but it can encounter a high computation load because of the inversion of matrix. In this work, a method which recursively updates the covariance matrix is proposed. The update scheme is selective by admitting data to the region requiring improvement. This enables the model to be updated without placing a high computation demand. In addition, the process is augmented by a pruning process which removes redundant data from the model to keep the data size compact. A mathematical example as well as an application to an industrial plant is presented to illustrate the applicability of the proposed method as well as to demonstrate its effectiveness in the prediction performance.

• 出版日期2013-12-25
• 单位浙江工业大学