Parameter identification of multi-input, single-output systems based on FIR models and least squares principle

By means of auxiliary models – finite impulse response (FIR) models, this paper develops an identification algorithm for multi-input, single-output stochastic systems. The basic idea is to estimate the FIR model parameters of each fictitious subsystem (submodel) with the FIR model orders increasing, and to use auxiliary models to predict/estimate the outputs of the submodels, and further to use the Pade approximation method to produce the parameter estimates of submodels. Some simulation results are given.