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

• 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. A key to the analysis and design of a dynamic system is to establish a suitable mathematical model of the system. This paper investigates the parameter optimization problem of a class of radial basis function-based multivariate hybrid models. Taking into account the high dimensions of the models and different forms of the parameters, the original identification model is separated into several regressive sub-identification models according to the characteristics of model outputs. Some auxiliary models are constructed to solve the unmeasurable noise terms in the information matrices. For the purpose of eliminating the redundant computation and to deal with the associate terms caused by the model decomposition, inspired by the coupling concept, a partially-coupled nonlinear parameter optimization algorithm is proposed for the multivariate hybrid models. Through the computational efficiency analysis and numerical simulation verification, it is shown that the proposed algorithm has low computational complexity and high parameter estimation accuracy. [ABSTRACT FROM AUTHOR]