Kashyap, SK and Raol, JR (2008) Neuronal Architectures for Mixed Estimation and Stabilized Output Error Methods. Journal of System Science and Engineering, System Society of India, 17 (2). pp. 29-36. ISSN 0972-5032
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Abstract
Some architectures based m recurrent neural networks for parameter estimation using least squares mixed estimation and stabilized output error principles are presented. Some error bounds of these neuronal-circuit architectures relative to conventional ones are derived. The results should be useful for implementation of parameter estimation methods for real time applications
Item Type: | Article |
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Uncontrolled Keywords: | Recurrent neural networks, parameter estimation, stabilized output error method, unstable/augmented systems |
Subjects: | MATHEMATICAL AND COMPUTER SCIENCES > Cybernetics, Artificial Intelligence and Robotics |
Depositing User: | Dr Sudesh Kumar Kashyap |
Date Deposited: | 29 Jun 2010 06:07 |
Last Modified: | 29 Jun 2010 06:07 |
URI: | http://nal-ir.nal.res.in/id/eprint/8454 |
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