Input Signals for System Identification

On the Design of Optimal Input Signals in System Identification

The problem of designing optimal inputs in the identification of linear systems with unknown random parameters is considered using a Bayesian approach. The information matrix, which is positive definite for the class of systems analyzed, gives a measure of performance for the system inputs. The computation of the optimal closed-loop input mappings is shown to be a nontrivial exercise in adaptiv...

Simple Signals for System Identification

Finite-Horizon Input Selection for System Identification∗

The accuracy of an identified model depends on the choice of input signal. Persistency of excitation is a necessary criterion for such signals. In this paper we develop additional criteria for input signal selection, in particular, the input at each time step is chosen to minimize the predicted variance of the system estimate at the next time step. We extend the method to the finitehorizon inpu...

Forecasting of rainfall using different input selection methods on climate signals for neural network inputs

Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and tem...

Noisy input-output system identification approach for time delay estimation

An unbiased impulse response estimation approach is applied to time delay estimation between noisy signals received at two spatially separated sensors. The estimated delay is modeled by an adaptive "nite impulse response "lter whose coe,cients are updated according to a modi"ed least mean square (LMS) scheme. Simulation results show that the proposed algorithm outperforms the conventional LMS t...