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ADAPTIVE NEURAL NETWORK BASED TARGET TRACKING. ADAPTIVE ESTIMATION FOR CONTROL OF UNCERTAIN NONLINEAR SYSTEMS WITH APPLICATIONS TO TARGET TRACKING

Автор:   Venkatesh M.
мягкая обложка
196 страниц
2009 год
Издательство:   Книга по Требованию
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Описание:While establishing global results, these approaches are only applicable to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. A novel approach to nonlinear state estimation, robust to unmodeled dynamics, is proposed from the perspective of augmenting an EKF with an NN based adaptive element. However, most of the NN based adaptive observers in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. The most popular approach is the extended Kalman filter (EKF) that goes through significant degradation in the presence of unmodeled nonlinearities. Design of nonlinear observers has received considerable attention since the early development of methods for state estimation. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown parameters where no apriori information about the unknown parameters is available.


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Study on decision support system for the interpretation of laboratory data by an artificial neural network // Rinsho. Byori.- 1994.- V.42, N.2.- P.195-199. 336. Pedrycz W. Neurocomputations in relational systems // IEEE Trans. Pattern Anal. and Mach. Intell.- 1991.- 13, № 3.- PP. 289-297. 337. Pham D.T., Liu X. Statespace identification of dynamic systems using neural networks // Eng. Appl. Artif. Intell.1990.- 3, № 3.- PP. 198-203. 338. Pineda F.J. Recurrent bakpropagation and the dynamical approach to adaptive neural computation. - Neural Comput., 1989. Vol. 1. PP.161–172. 339. Poli R., Cagnoni S., Livi R. et al. A Neural Network Expert System for Diagnosing and Treating Hypertension // Computer.- 1991.- N.3.- P.64-71. 340. Prechelt L. Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping // Progress in Neural Information Processing (Hong Kong, September 24-27, 1996), Springer, Vol. 1 pp. 46-52. 341. Real Brains, Artificial Minds/Ed. by J.L. Casti, A. Karlqvist. Norton-Holland, 1987. 226 p. 342. Reinbnerger G., Weiss G., Werner-Felmayer G. et al ...»

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