Hopfield neural networks for state estimation: parameters, efficient implementation and results |
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Authors: | F. García-Lagos G. Joya F. J. Marín F. Sandoval |
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Affiliation: | 1. Dpto. Technología Electrónica E.T.S.I. Telecomunicación, Universidad de Málaga, Campus Teatinos s/n, E-29071, Málaga, Spain 2. Dpto. Electrónica E.T.S.I. Informática, Universidad de Málaga, Campus Teatinos s/n, E-29071, Málaga, Spain
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Abstract: | 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. |
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