Parameter estimation of state space models by recurrent neural networks

Raol, JR (1995) Parameter estimation of state space models by recurrent neural networks. IEE Proceedings, 124 (2). pp. 114-118.

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Abstract

Four variants of recurrent neural networks (RNNs) are studied. The similarities and contradistinction of these formulations are brought out from the view point of their applicability to parameter estimation in dynamic systems. The trajectory matching algorithms are also given. A recursive information processing scheme within the structure of Hopfield neural network for parameter estimation is presented. Numerical simulation results for non recursive and recursive schemes are given.

Item Type: Journal Article
Uncontrolled Keywords: State space;Models;Recurrent neural networks;Hopefield neural network
Subjects: MATHEMATICAL AND COMPUTER SCIENCES > Mathematical and Computer Scienes(General)
Division/Department: Flight Mechanics and Control Division
Depositing User: M/S ICAST NAL
Date Deposited: 25 Mar 2008
Last Modified: 24 May 2010 09:55
URI: http://nal-ir.nal.res.in/id/eprint/4589

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