Abstract
A class of self-optimizing systems which continually alter their parameters to reduce a mean-square performance criterion is described. The change in each parameter is determined from an error gradient in parameter space computed by cross-correlation methods which are independent of signal spectra and require no test signal or parameter perturbation. Applications of this technique to both open-loop and closed-loop systems are included and it is shown that a combination of such self-optimizing systems is a possible solution to the adaptive control problem. Computer simulation results are included to demonstrate the practicality of the proposed systems.
Keywords
Control theory (sociology)Computer scienceCorrelationParameter spaceSIGNAL (programming language)Perturbation (astronomy)Open-loop controllerClosed loopMathematical optimizationAlgorithmMathematicsControl engineeringStatisticsControl (management)Artificial intelligenceEngineeringPhysics
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Publication Info
- Year
- 1964
- Type
- article
- Volume
- 9
- Issue
- 1
- Pages
- 31-38
- Citations
- 56
- Access
- Closed
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Cite This
Kumpati S. Narendra,
L. McBride
(1964).
Multiparameter self-optimizing systems using correlation techniques.
IEEE Transactions on Automatic Control
, 9
(1)
, 31-38.
https://doi.org/10.1109/tac.1964.1105638
Identifiers
- DOI
- 10.1109/tac.1964.1105638