Sensitivity of structures for the identification of linear systems from impulse response data

The authors investigate the usefulness of structures with high parameter sensitivities for the purpose of increasing the convergence rate of parameter identification algorithms. In particular, they examine two SISO (single input, single output) structures: the direct form II and the dual generalized Hessenberg representation. A Gauss-Newton identification procedure is presented for implementing parameter identification from impulse response data. The authors then present a sensitivity analysis and provide some illustrative examples