๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Neural networks and the best trigomometric approximation

โœ Scribed by Jianjun Wang; Zongben Xu

Book ID
107347282
Publisher
Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Year
2011
Tongue
English
Weight
442 KB
Volume
24
Category
Article
ISSN
1009-6124

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Networks and the best approximation prop
Networks and the best approximation property
โœ F. Girosi; T. Poggio ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› Springer-Verlag ๐ŸŒ English โš– 815 KB

Networks can be considered as approximation schemes. Multilayer networks of the perceptron type can approximate arbitrarily well continuous functions (Cybenko 1988(Cybenko , 1989;;Funahashi 1989;Stinchcombe and White 1989). We prove that networks derived from regularization theory and including Radi

Random Approximants and Neural Networks
Random Approximants and Neural Networks
โœ Y. Makovoz ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 492 KB

Let D be a set with a probability measure +, +(D)=1, and let K be a compact subset of L q (D, +), where the infimum is taken over all g n of the form g n = n i=1 a i , i , with arbitrary , i # K and a i # R. It is shown that for f # conv(K \_ (&K )), under some mild restrictions, \ n ( f, K ) C q =

The fuzzy neural network approximation l
The fuzzy neural network approximation lemma
โœ Thomas Feuring; Wolfram-M. Lippe ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 732 KB

It is well known that artificial neural networks are universal approximators. But what about fuzzy neural networks? Only Buckl, ey and Hayashi [1] presented a theoretical result for these networks: They showed that there are fuzzy functions which cannot be approximated by a certain fuzzy neural netw