DocumentCode
2409283
Title
A neurocomputing algorithm for linear state estimation
Author
Alouani, A.T. ; Sun, Q.
Author_Institution
Tennessee Technol. Univ., Cookeville, TN, USA
fYear
1992
fDate
1992
Firstpage
2702
Abstract
A linearized Hopfield neural network (HNN) is used as a computing tool to solve a continuous-time linear state estimation problem. The estimation problem is treated as a dynamic optimization problem, where the objective is to find the system state that optimizes a performance measure. It is shown that, by appropriate choice of the weights of the HNN, the optimal state can be obtained as the sealed output of an HNN
Keywords
Hopfield neural nets; State estimation; state estimation; continuous-time linear state estimation problem; linearized Hopfield neural network; neurocomputing algorithm; Computer networks; Error analysis; Filtering theory; Filters; Gaussian distribution; Hopfield neural networks; Large Hadron Collider; State estimation; Statistical distributions; Stochastic processes; Sun; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location
Tucson, AZ
Print_ISBN
0-7803-0872-7
Type
conf
DOI
10.1109/CDC.1992.371327
Filename
371327
Link To Document