A recursive algorithm for nonlinear model identification

Abstract

A recursive algorithm for nonlinear dynamic system identification is studied in this paper. The structure and parameters can be obtained simultaneously and recursively not only for the linear-in-the-parameters models, but applicable to general nonlinear system. It is based on the ‘innovation’ idea and net information contribution criteria. Using the recursive formulae for computing the Moore–Penrose inverse of a matrix, it is possible to combine innovation calculation with structure term determination and parameters estimation. The recursive algorithm has the advantage of computational simplicity. Simulation examples are given which show the proposed method is numerically more stable than the approach existed.