A Parameter Optimization Method for Radial Basis Function Type Models  

Oleh:

Ozaki, T. ; Toyoda, Y. ; Peng, Hu ; Haggan-Ozaki, V.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 2 (2003), page 432-438.
Topik: radial basis function network; parameter; optimization method; radial basis function; type models

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

This paper considers the non linear systems modeling problem for control. A structured non linear parameter optimization method (SNPOM) adapted to radial basis function (RBF) networks and an RBF network - style coefficients autoregressive model with exogenous variable model parameter estimation is presented. This is an off - line nonlinear model parameter optimization method, depending partly on the Levenberg - Marquardt method for nonlinear parameter optimization and partly on the least - squares method using singular value decomposition 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.

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