Recursive Nonlinear Identification

Abstract

The system identification procedure mainly consists of model structure selection and parameter estimation. The former is concerned with selecting which class of mathematical operator is to be used as a model. The latter is concerned with an estimation algorithm and usually requires input output data from the process, a class of models to be identified and a suitable identification criterion. A number of techniques have been developed in recent years for model selection and parameter estimation of nonlinear systems. Forward and backward regression algorithms were analysed in Leontaritis and Billings (1987). Stepwise regression was used in Billings and Voon (1986) and a class of orthogonal estimators were discussed in Korenberg et al. (1988). Algorithms with the objective of saving memory and allowing fast computation have been proposed in Chen and Wigger (1995). Methods to determine the a priori structural identifiability of a model have also been studied (Ljung and Glad, 1994). A survey of existing techniques of nonlinear system identification prior to the 1980s is given in Billings (1980), a survey of the structure detection of input output nonlinear systems is given in Haber and Unbehauen (1990) and a survey of nonlinear black-box modelling in system identification can be found in Sjoberg et al. (1995).