For the modeling issue of non-uniformly multi-rates sampled nonlinear systems

a fuzzy identification method based on hierarchical principle is proposed in the paper. First of all

a nonlinear system is described as a weighted combination representation of the multiple local linear models by using lift technology when the non-uniformly updating scheme for input signals and uniformly sampling scheme for output signals are taken in the data sampling process. On this basis

we propose a fuzzy identification algorithm

in which the GK fuzzy clustering method and a recursive least squared method based on hierarchical principle are used to confirm the premise structure and consequence parameters of fuzzy model

respectively. Moreover

the convergence of the identification algorithm is studied by using martingale theorem. Finally

the effectiveness of the proposed method is demonstrated by a simulation example.