Hopfield neural networks for state estimation: parameters, efficient implementation and results
Autor: | Francisco Sandoval, Francisco García-Lagos, F. J. Marín, Gonzalo Joya |
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Rok vydání: | 2000 |
Předmět: | |
Zdroj: | e & i Elektrotechnik und Informationstechnik. 117:4-7 |
ISSN: | 1613-7620 0932-383X |
Popis: | State estimation processes measurements and other information to find the network state vector. In this paper, state estimation is considered as an optimization problem to be solved with a Hopfield neural network. Several activation models for this network are simulated and compared. A new method is proposed that calculates the integration step parameter for this network in an autonomous way, eliminating the need for determining it in a manual way for each particular problem. This algorithm has been successfully tested for a wide range of electrical nets. Neural and classic analytical methods are compared. |
Databáze: | OpenAIRE |
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