In this paper, we give a universal approach to approximation of non-linear functionals and so called myopic input-output maps by neural network-like architectures. Strong theorems on equi-uniform approximation to functionals in abstract spaces are given. As applications, theorems on identification o
β¦ LIBER β¦
Neural Networks: A General Framework for Non-Linear Function Approximation
β Scribed by Manfred M Fischer
- Book ID
- 111021830
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 177 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1361-1682
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
A unified approach for neural network-li
β
Tianping Chen
π
Article
π
1998
π
Elsevier Science
π
English
β 56 KB
A max-piecewise-linear neural network fo
β
Chengtao Wen; Xiaoyan Ma
π
Article
π
2008
π
Elsevier Science
π
English
β 394 KB
Toward generating neural network structu
β
Tarek M. Nabhan; Albert Y. Zomaya
π
Article
π
1994
π
Elsevier Science
π
English
β 893 KB
A general framework for functional netwo
β
Castillo, Enrique; Cobo, Angel; GοΏ½mez-Nesterkin, RuslοΏ½n; Hadi, Ali S.
π
Article
π
2000
π
John Wiley and Sons
π
English
β 419 KB
π 2 views
In this paper, we introduce functional networks as a generalization and extension of the standard neural networks in the sense that every problem that can be solved by a neural network can also be formulated by a functional network. But, more importantly, we give examples of problems that cannot be
Modular neural networks for non-linearit
β
Z. Hasiewicz
π
Article
π
2000
π
Elsevier Science
π
English
β 574 KB
Piecewise linear approximation applied t
β
Amin, H.; Curtis, K.M.; Hayes-Gill, B.R.
π
Article
π
1997
π
The Institution of Electrical Engineers
π
English
β 556 KB