DocumentCode
1902344
Title
A universal structure for artificial neural networks
Author
Rauf, Fawad ; Ahned, H.M.
Author_Institution
Dept. of Electr. & Comput. Sci., Boston Univ., MA, USA
fYear
1993
fDate
1993
Firstpage
15
Abstract
An approximation procedure, named successive linearization, is introduced for unified implementation of a large class of neural networks. A nonlinear neural model with dynamic sensitivity is presented. It is modular and has rapid learning schemes. Arbitrary nonlinear functions with memory which are commonly used for modeling dynamical systems, as well as static nonlinear classification boundaries, can both be implemented equally well. Fast learning algorithms for the universal structure are presented
Keywords
learning (artificial intelligence); neural nets; approximation procedure; artificial neural networks; dynamic sensitivity; nonlinear functions; rapid learning schemes; static nonlinear classification boundaries; successive linearization; Artificial neural networks; Associative memory; Geometry; Laboratories; Linear approximation; Multi-layer neural network; Neural networks; Neurons; Parameter estimation; Polynomials;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993., IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0999-5
Type
conf
DOI
10.1109/ICNN.1993.298538
Filename
298538
Link To Document