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https://scholarbank.nus.edu.sg/handle/10635/72802
DC Field | Value | |
---|---|---|
dc.title | Nonlinear systems identification using RBF neural networks | |
dc.contributor.author | Tan, Shaohua | |
dc.contributor.author | Hao, Jianbin | |
dc.contributor.author | Vandewalle, Joos | |
dc.date.accessioned | 2014-06-19T05:12:08Z | |
dc.date.available | 2014-06-19T05:12:08Z | |
dc.date.issued | 1993 | |
dc.identifier.citation | Tan, Shaohua,Hao, Jianbin,Vandewalle, Joos (1993). Nonlinear systems identification using RBF neural networks. Proceedings of the International Joint Conference on Neural Networks 2 : 1833-1836. ScholarBank@NUS Repository. | |
dc.identifier.isbn | 0780314212 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/72802 | |
dc.description.abstract | We present a recursive nonlinear identification technique based on feedforward neural networks. A distinct feature of the proposed technique is the use of Radial-Basis-Function (RBF) neural nets as generic discrete nonlinear model structure. RBF nets have enabled us to devise a stable weight updating algorithm that guarantees the convergence of the weights to the target values. A simulation example is provided to illustrate the effectiveness of the method. | |
dc.source | Scopus | |
dc.type | Conference Paper | |
dc.contributor.department | ELECTRICAL ENGINEERING | |
dc.description.sourcetitle | Proceedings of the International Joint Conference on Neural Networks | |
dc.description.volume | 2 | |
dc.description.page | 1833-1836 | |
dc.description.coden | 85OFA | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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