A selective recursive ridge extreme learning machine is proposed for online identification of nonlinear systems. First

a recursive algorithm of ridge extreme learning machine with nodes growing is formulated

which can update the online model in an efficient manner. Additionally

by incorporating the relative predictive error of the training set

a strategy by selectively increasing nodes is proposed to restrict the complexity of the identification model. Consequently

a more simple identification model with the recursive update manner can be obtained. Multi-fold simulations on a benchmark nonlinear chemical process have been investigated. And the comparison results verify the simplicity and superiority of the proposed approach

which is more suitable for online identification of nonlinear systems.