Name
Title | ||
|---|---|---|
Efficient recursive algorithms for detection of abrupt changes in signals and control systems. |
Abstract | ||
|---|---|---|
This paper addresses a number of open problems concerning the generalized likelihood ratio (GLR) rules for online detection of faults and parameter changes in control systems. It is shown that with an appropriate choice of the threshold and window size, these GLR rules are asymptotically optimal. The rules are also extended to non-likelihood statistics that are widely used in monitoring adaptive algorithms for system identification and control by establishing Gaussian approximations to these statistics when the window size is chosen suitably. Recursive algorithms are developed for practical implementation of the procedure, and importance sampling techniques are introduced for determining the threshold of the rule to satisfy prescribed bounds on the false alarm rate. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/9.763211 | IEEE Trans. Automat. Contr. |
Keywords | Field | DocType |
Control systems,Signal processing algorithms,Fault detection,Parametric statistics,Monte Carlo methods,Adaptive control,Stochastic processes,System identification,Gaussian approximation,Stochastic systems | Importance sampling,Mathematical optimization,Recursion (computer science),Detection theory,Algorithm,Constant false alarm rate,Estimation theory,Adaptive control,System identification,Asymptotically optimal algorithm,Mathematics | Journal |
Volume | Issue | ISSN |
44 | 5 | 0018-9286 |
Citations | PageRank | References |
28 | 4.89 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tze Leung Lai | 1 | 89 | 15.87 |
| J. Z. Shan | 2 | 28 | 4.89 |