Identification of Linear and Nonlinear Continuous Time Models from Sampled Data Sets

摘要

The identification of continuous time linear and nonlinear models from sampled-data records is investigated and a new recursive orthogonal algorithm is derived. An integral equation formulation is shown to be appropriate and enhancement of the parameter estimates using data prefiltering is demonstrated for noise corrupted data sets. Simulated examples are included to illustrate the methods.