Name
Title | ||
|---|---|---|
Criteria For Determining The Optimal Levels Of Multilevel Perturbation Signals For Nonlinear System Identification |
Abstract | ||
|---|---|---|
A method is developed for determining the optimal levels of multilevel perturbation signals for nonlinear system identification, using the condition numbers of matrices derived from a Vandermonde matrix of the set of signal levels. It is applicable to the identification of nonlinear systems when the perturbation signal is applied directly to a static nonlinearity. The optimal signal level sets of size q obtained when the order of the nonlinearity is q - 1 are virtually identical to those obtained previously for Volterra series models by a more complex method. With the new method, optimal signal level sets can also be obtained for every order of nonlinearity less than q - 1, in most of which the number of different signal levels is less than the signal level set size q. The results indicate that, for nonlinear system identification, a confidence limit is reached when 7-level signals are used for the identification of 6-th order nonlinearities. They also show that, for the identification of an r-th order nonlinearity, there is little point in using signals with more than r + 1 different levels, although in most cases the size of the optimal signal level set that contains these levels will be,greater than r + 1. The method gives optimal signal level sets that are independent of the number of occurrences of the signal level set during a measurement period. Their values are shown to be the global optima for pseudo-random perturbation signals derived from maximum-length sequences, in which the zero level occurs one time less than the other levels during a period. For periods of 100 or more, the differences between the actual and global optima are less than 1%. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ACC.2003.1240533 | PROCEEDINGS OF THE 2003 AMERICAN CONTROL CONFERENCE, VOLS 1-6 |
Keywords | DocType | ISSN |
sequences,vandermonde matrix,nonlinear system identification,robustness,confidence limit,identification,nonlinear systems,maximum length sequence,nonlinear equations,level set,signal processing,nonlinear system,condition number | Conference | 0743-1619 |
Citations | PageRank | References |
3 | 0.61 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. A. Barker | 1 | 5 | 2.07 |
| Ai Hui Tan | 2 | 95 | 13.21 |
| K. R. Godfrey | 3 | 68 | 18.03 |