Abstract
This paper presents a novel neuro-computing approach to the problem of state estimation by means of an hybrid combination of Hopfield neural network whose capability of solving certain optimization problems is well-known and feedforward multilayer neural net which is very popular because of its universal approximation property. This neuro-estimator is very appropriate for the real-time implementation of linear or/and especially nonlinear state estimators. Simulation results shows the effectiveness of the proposed method.
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© 1997 Springer-Verlag Berlin Heidelberg
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Menhaj, M.B., Salmasi, F.R. (1997). A novel neurocomputing approach to nonlinear stochastic state estimation. In: Reusch, B. (eds) Computational Intelligence Theory and Applications. Fuzzy Days 1997. Lecture Notes in Computer Science, vol 1226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62868-1_97
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DOI: https://doi.org/10.1007/3-540-62868-1_97
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