Title :
A neurocomputing algorithm for linear state estimation
Author :
Alouani, A.T. ; Sun, Q.
Author_Institution :
Tennessee Technol. Univ., Cookeville, TN, USA
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;
Conference_Titel :
Decision and Control, 1992., Proceedings of the 31st IEEE Conference on
Conference_Location :
Tucson, AZ
Print_ISBN :
0-7803-0872-7
DOI :
10.1109/CDC.1992.371327
