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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1099
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| DC Field | Value | Language |
|---|
| contributor.author | Subudhi, B | - |
| contributor.author | Jena, D | - |
| date.accessioned | 2009-12-07T08:59:10Z | - |
| date.available | 2009-12-07T08:59:10Z | - |
| date.issued | 2009 | - |
| identifier.citation | IEEE TENCON 2009, 23-26 November 2009, Singapore | en |
| identifier.uri | http://hdl.handle.net/2080/1099 | - |
| description | copyright belongs to TENCON | en |
| description.abstract | This work presents system identification using neural
network approaches for modelling a laboratory based twin rotor
multi-input multi-output system (TRMS). Here we focus on a
memetic algorithm based approach for training the multilayer
perceptron neural network (NN) applied to nonlinear system
identification. In the proposed system identification scheme, we
have exploited three global search methods namely genetic
algorithm (GA), Particle Swarm Optimization (PSO) and
differential evolution (DE) which have been hybridized with the
gradient descent method i.e. the back propagation (BP) algorithm
to overcome the slow convergence of the evolving neural
networks (EANN). The local search BP algorithm is used as an
operator for GA, PSO and DE. These algorithms have been
tested on a laboratory based TRMS for nonlinear system
identification to prove their efficacy. | en |
| format.extent | 252853 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | IEEE | en |
| subject | Differential evolution | en |
| subject | Evolutionary computation | en |
| subject | Nonlinear system identification | en |
| subject | Back propagation | en |
| subject | Twin rotor system | en |
| title | Nonlinear System Identification of A Twin Rotor MIMO System | en |
| type | Article | en |
| Appears in Collections: | Conference Papers
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| File |
Description |
Size | Format |
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| bks2.pdf | | 246Kb | Adobe PDF | View/Open |
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