๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Toward generating neural network structures for function approximation

โœ Scribed by Tarek M. Nabhan; Albert Y. Zomaya

Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
893 KB
Volume
7
Category
Article
ISSN
0893-6080

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

Cubic approximation neural network for m
Cubic approximation neural network for multivariate functions
โœ Doron Stein; Arie Feuer ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 374 KB

This paper introduces a novel neural network architecture-cubic approximation neural network (CANN), capable of local approximation of multivariate functions. It is particularly simple in concept and in structure. Its simplicity enables a quantitative evaluation of its approximation capabilities, na

Design of function-generating mapping ne
Design of function-generating mapping networks by interactive neural-network simulation
โœ Granino A. Korn ๐Ÿ“‚ Article ๐Ÿ“… 1991 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 812 KB

We apply a new interactive simulation environment for neural-network development to the development of mapping networks, which produce learned or preset functions of real inputs. Function-mapping networks are useful for adaptive control and as general-purpose, self-learning function generators. DESI

Properties and performance of orthogonal
Properties and performance of orthogonal neural network in function approximation
โœ Chieh F. Sher; Ching-Shiow Tseng; Chen-San Chen ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 216 KB

Backpropagation neural network has been applied successfully to solving uncertain problems in many fields. However, unsolved drawbacks still exist such as the problems of local minimum, slow convergence speed, and the determination of initial weights and the number of processing elements. In this pa

Orthogonal considerations in the design
Orthogonal considerations in the design of neural networks for function approximation
โœ B. Francois ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 604 KB

Two problems occur in the design of feedforward neural networks: the choice of the optimal architecture and the initialization. Generally, input and output data of a system (or a function) are measured and recorded. Then, experimenters wish to design a neural network to map exactly these output valu