Approximation of Nonlinear Systems With Radial Basis Function Neural Networks  

Oleh:

Schilling, R. J. ; Carroll, J. J. ; Al-Ajlouni, Ahmad F.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 1 (2001), page 1-15.
Topik: radial basis function network; approximation; non linear systems; radial basis function; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented. The method uses an n - dimensional raised - cosine type of RBF that is smooth, yet has compact support. The RBF network coefficients are low - order polynomial functions of the input. A simple computational procedure is presented which significantly reduces the network training and evaluation time. Storage space is also reduced by allowing for a nonuniform grid of points about which the RBFs are centered. The network output is shown to be continuous and have a continuous first derivative. When the network is used to approximate a nonlinear dynamic system, the resulting system is bounded - input bounded - output stable. For the special case of a linear system, the RBF network representation is exact on the domain over which it is defined, and it is optimal in terms of the number of distinct storage parameters required. Several examples are presented which illustrate the effectiveness of this technique.

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