Partially-coupled nonlinear parameter optimization algorithm for a class of multivariate hybrid models

作者：

Highlights：

• The original identification model is decomposed into several sub models according to the dimension of output and different forms of parameters.

• To solve the unmeasurable noise terms in the information matrices, we construct some auxiliary models based on the obtained parameter estimates.

• To cut down the redundant estimates and solve the associate terms , a partially coupled nonlinear parameter optimization algorithm is proposed.

摘要

•The original identification model is decomposed into several sub models according to the dimension of output and different forms of parameters.•To solve the unmeasurable noise terms in the information matrices, we construct some auxiliary models based on the obtained parameter estimates.•To cut down the redundant estimates and solve the associate terms , a partially coupled nonlinear parameter optimization algorithm is proposed.

论文关键词：Radial basis function,Hybrid model,Gradient search,Nonlinear optimization,Coupling identification

论文评审过程：Received 7 May 2021, Revised 9 August 2021, Accepted 15 September 2021, Available online 28 September 2021, Version of Record 28 September 2021.

论文官网地址：https://doi.org/10.1016/j.amc.2021.126663