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: | Journal 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 |
| Division/Department: | Flight Mechanics and Control Division, Other |
| Depositing User: | M/S ICAST NAL |
| Date Deposited: | 22 Feb 2008 |
| Last Modified: | 17 Jun 2010 10:28 |
| URI: | http://nal-ir.nal.res.in/id/eprint/4620 |
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