2003年3月
A parameter optimization method for radial basis function type models
IEEE TRANSACTIONS ON NEURAL NETWORKS
- ,
- ,
- ,
- 巻
- 14
- 号
- 2
- 開始ページ
- 432
- 終了ページ
- 438
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1109/TNN.2003.809395
- 出版者・発行元
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
This paper considers the nonlinear systems modeling problem for control. A structured nonlinear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network-style coefficients AutoRegressive model with eXogenous variable (RBF-ARX) model parameter estimation is presented. This is an off-line nonlinear model parameter optimization method, depending partly on the Levenberg-Marquardt method (LMM) for nonlinear parameter optimization and partly on the least-squares method (LSM) using singular value decomposition (SVD) for linear parameter estimation. When. compared with some other algorithms, the SNPOM accelerates the computational convergence of the parameter optimization search process of RBF-type models. The usefulness of this approach is illustrated by means of several examples.
- リンク情報
- ID情報
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- DOI : 10.1109/TNN.2003.809395
- ISSN : 1045-9227
- eISSN : 1941-0093
- Web of Science ID : WOS:000181820400017