NDLI: A new incremental method for function approximation using feed-forward neural networks

Content Provider	IEEE Xplore Digital Library
Author	Romero, E. Alquezar, R.
Copyright Year	2002
Description	Author affiliation: Dept. de Llenguatges i Sistemes Inf., Univ. Politecnica de Catalunya, Barcelona, Spain (Romero, E.; Alquezar, R.)
Abstract	A sequential method for approximating vectors in Hilbert spaces, called sequential approximation with optimal coefficients and interacting frequencies (SAOCIF), is presented. SAOCIF combines two key ideas. The first one is the optimization of the coefficients. The second one is the flexibility to choose the frequencies. The approximations defined by SAOCIF maintain orthogonal-like properties. The theoretical results obtained prove that, under reasonable conditions, the residue of the approximation obtained with SAOCIF (in the limit) is the best one that can be obtained with any subset of the given set of vectors. In the particular case of L/sup 2/, it can be applied to approximations by algebraic polynomials, Fourier series, wavelets and feed-forward neural networks, among others. Also, a particular algorithm with feed-forward neural networks is presented. The method combines the locality of sequential approximations, where only one frequency is found at every step, with the globality of non-sequential ones, where every frequency interacts with the others. Experimental results show a very satisfactory performance.
Sponsorship	IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc
Starting Page	1968
Ending Page	1973
File Size	684472
Page Count	6
File Format	PDF
ISBN	0780372786
ISSN	10987576
DOI	10.1109/IJCNN.2002.1007821
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Function approximation Feedforward systems Neural networks Feedforward neural networks Hilbert space Vectors Frequency Fourier series Signal processing algorithms Statistics
Content Type	Text
Resource Type	Article

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