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Neural networks for nonlinear state estimation
Author(s) -
Parisini T.,
Zoppoli R.
Publication year - 1994
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4590040202
Subject(s) - nonlinear system , estimator , computer science , artificial neural network , state (computer science) , feedforward neural network , mathematical optimization , reduction (mathematics) , feed forward , control theory (sociology) , estimation , control (management) , algorithm , mathematics , artificial intelligence , control engineering , engineering , statistics , physics , geometry , systems engineering , quantum mechanics
Abstract Estimating the state of a nonlinear stochastic system (observed through a nonlinear noisy measurement channel) has been the goal of considerable research to solve both filtering and control problems. In this paper, an original approach to the solution of the optimal state estimation problem by means of neural networks is proposed, which consists in constraining the state estimator to take on the structure of a multilayer feedforward network. Both non‐recursive and recursive estimation schemes are considered, which enable one to reduce the original functional problem to a nonlinear programming one. As this reduction entails approximations for the optimal estimation strategy, quantitative results on the accuracy of such approximations are reported. Simulation results confirm the effectiveness of the proposed method.