Title :
Reduced gradient computation in prediction error identification
Author_Institution :
Johns Hopkins University, Laurel, MD, USA
fDate :
8/1/1985 12:00:00 AM
Abstract :
One way to design practical identification algorithms using the prediction error method is to find a computationally efficient expression for the gradient of the prediction error cost function. Here, results on adjoint equations are used to derive an efficient gradient expression which reduces the computational burden of evaluating it.
Keywords :
Gradient methods; Parameter estimation, linear systems; Prediction methods; System identification, linear systems; Equations; Filtering; Filters; Noise measurement; Noise reduction; Optimal control; State estimation; Steady-state; Time varying systems; Uncertain systems;
Journal_Title :
Automatic Control, IEEE Transactions on
DOI :
10.1109/TAC.1985.1104062