|
EPrints@IIT Delhi >
Faculty Research Publicatons >
Electrical Engineering >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/2081
|
Full metadata record
| DC Field | Value | Language |
|---|
| contributor.author | Purwar, S | - |
| contributor.author | Kar, I N | - |
| contributor.author | Jha, A N | - |
| date.accessioned | 2006-08-08T03:27:43Z | - |
| date.available | 2006-08-08T03:27:43Z | - |
| date.issued | 2003 | - |
| identifier.citation | TENCON Conference on Convergent Technologies for Asia-Pacific Region, 3, 1115 - 1119p. | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/2081 | - |
| description.abstract | This paper proposes a computationally efficient artificial neural network (ANN) model for system identification of unknown dynamic nonlinear continuous and discrete time systems. A single layer functional link ANN is used for the model where the need of hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. These models are linear in their parameters. The recursive least squares method with forgetting factor is used as on-line learning algorithm for parameter updating. The good behaviour of the identification method is tested on two single input single output (SISO) continuous time plants and two discrete time plants. Stability of the identification scheme is also addressed. | en |
| format.extent | 57405 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | artificial neural network | en |
| subject | single input single output | en |
| title | On-line system identification using Chebyshev neural networks | en |
| type | Article | en |
| Appears in Collections: | Electrical Engineering
|
Files in This Item:
| File |
Description |
Size | Format |
|---|
| purwarlin2003.pdf | | 56Kb | Adobe PDF | View/Open |
|
Show simple item record
All items in DSpace are protected by copyright, with all rights reserved.
|
