Name
Abstract | ||
|---|---|---|
Pseudo-random signals have been widely used for system identification. Maximum length binary signals are the best known class of pseudo-random signals, because of their ease of generation using feedback shift registers, but it is less well known that there are several other classes of binary and near-binary signals with identical, or nearly identical properties. An overview of these classes of signal is given and the design of a new MATLAB routine incorporating all these classes of signal is described. The importance of the choice of MLB signal to use in particular applications is illustrated with the identification of a Wiener system having a quadratic nonlinearity and a cubic nonlinearity. Errors in the measurements of the input-output crosscorrelation function caused by the nonlinearities can be reduced when the signal is used in the estimation of the system weighting function if an appropriate choice of feedback connections and data length used for the estimation are chosen. In the final part of the paper, three measures of signal quality are considered |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/TIM.2002.802243 | Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE |
Keywords | Field | DocType |
correlation analysis,perturbation signals,quadratic nonlinearity,linear system weighting function analysis,stochastic processes,maximum length binary signals,identification,binary sequences,system identification,perturbation techniques,binary pseudorandom signals,feedback connections,pseudorandom signal class overview,circuit feedback,nonlinear systems,estimation data length,shift registers,wiener system,near-binary pseudorandom signal generation,matlab routines,feedback shift registers,cubic nonlinearity,nonlinearity error reduction,correlation methods,electronic engineering computing,signal processing,frequency domain analysis,indexing terms,autocorrelation,signal generators,weight function,linear system,feedback | Shift register,Weighting,Nonlinear system,Linear system,Stochastic process,Electronic engineering,Control engineering,System identification,Mathematics,Binary number,Pseudorandom number generator | Journal |
Volume | Issue | ISSN |
51 | 4 | 0018-9456 |
ISBN | Citations | PageRank |
0-7803-6646-8 | 18 | 1.91 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ai Hui Tan | 1 | 95 | 13.21 |
| K. R. Godfrey | 2 | 68 | 18.03 |