Fitting of multivariate functions

1987 ◽  
Vol 75 (7) ◽  
pp. 970-971 ◽  
Author(s):  
A.A. Georgiev
Keyword(s):  
Multivariate Functions

Automated establishment and plotting of multivariate functions

1990 ◽  
Vol 234 ◽  
pp. 493-497
Author(s):  
Zequn Yan ◽  
Xiaoheng Tang
Keyword(s):  
Multivariate Functions

scholarly journals Decoupling multivariate functions using a non-parametric Filtered CPD approach

2021 ◽  
Vol 54 (7) ◽  
pp. 451-456
Author(s):  
Jan Decuyper ◽  
Koen Tiels ◽  
Siep Weiland ◽  
Johan Schoukens
Keyword(s):  
Multivariate Functions ◽  
Non Parametric

Theoretical Analyses of the Universal Approximation Capability of a Class of Higher Order Neural Networks Based on Approximate Identity

2016 ◽  
pp. 1456-1470 ◽  
Author(s):  
Saeed Panahian Fard ◽  
Zarita Zainuddin
Keyword(s):  
Neural Networks ◽  
Approximation Theory ◽  
Special Class ◽  
Higher Order ◽  
Approximate Identity ◽  
Universal Approximation ◽  
Multivariate Functions ◽  
Higher Order Neural Networks ◽  
Approximation Capability ◽  
Theoretical Analyses

One of the most important problems in the theory of approximation functions by means of neural networks is universal approximation capability of neural networks. In this study, we investigate the theoretical analyses of the universal approximation capability of a special class of three layer feedforward higher order neural networks based on the concept of approximate identity in the space of continuous multivariate functions. Moreover, we present theoretical analyses of the universal approximation capability of the networks in the spaces of Lebesgue integrable multivariate functions. The methods used in proving our results are based on the concepts of convolution and epsilon-net. The obtained results can be seen as an attempt towards the development of approximation theory by means of neural networks.

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