Neural network architectures for parameter estimation of dynamical systems

Raol, JR and Madhuranath, H (1996) Neural network architectures for parameter estimation of dynamical systems. IEE Proceedings: Control Theory and Applications, 143 (4). pp. 387-394.

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Abstract

Various recurrent neural network architectures for solving the problems of parameter estimation in dynamical systems are presented. The architectures based on precomputation of weight/bias information (Hopfield neural network), direct gradient computation with and without normalisation and output error method are developed. A typical computer simulation result is given.

Item Type: Article
Additional Information: Copyright for this article belongs to IEE
Uncontrolled Keywords: Neural network;Parameter estimation;Parallel computing; Dynamical system
Subjects: MATHEMATICAL AND COMPUTER SCIENCES > Systems analysis and Operations Research
Depositing User: Users 90 not found.
Date Deposited: 22 Feb 2008
Last Modified: 17 Jun 2010 04:58
URI: http://nal-ir.nal.res.in/id/eprint/4620

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