Multiplication-Free Radial Basis Function Network  

Oleh:

Kampl, S. ; Heiss, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 7 no. 6 (1996), page 1461-1464.
Topik: radial basis function network; multiplication - free; radial basis; function network

Ketersediaan

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

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Isi artikel

For the purpose of adaptive function approximation, a new radial basis function network is proposed which is nonlinear in its parameters. The goal is to reduce significantly the computational effort for a serial processor, by avoiding multiplication in both the evaluation of the function model and the computation of the parameter adaptation. The approximation scheme makes use of a grid-based Gaussian basis function network. Due to the local support of digitally implemented Gaussian functions the function representation is parametric local and therefore well suited for an online implementation on a microcomputer. A gradient descent based non linear learning algorithm is presented and the convergence of the algorithm is proved.

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