Identification of Systems from Non-Uniformly Sampled Data

摘要

The identification of continuous time models from uniformly sampled data recordsis investigated and an identification algorithm based on the state variable filter approach is derived. The orthogonal least squares estimator is shown to be adaptable for the identification of continuous time models from nonuniformly sampled data records and instrumental variables are introduced to reduce the bias on stochastic system identification. Multiplying the filtered variables obtained from the state variable filter with higher powers of the noise free output signal prior to the estimation is shown to enhance the parameter estimates. Simulated examples are concluded to illustrate the methods.