DocumentCode :
3248148
Title :
An artificial neural networks for approximating polynomial functions
Author :
Malakooti, Behnam ; Zhou, YingQing
Author_Institution :
Dept. of Syst. Eng., Case Western Reserve Univ., Cleveland, OH, USA
Volume :
3
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
966
Abstract :
The authors use polynomial function as a common base to measure the capacity of a feedforward artificial neural network (FANN) with a finite number of hidden nodes. They show that there is a relationship between the capacity of a FANN in approximating polynomial functions and the number of hidden nodes used in the FANN. A procedure for realizing a FANN in approximating polynomial functions is described. Two examples are given to show the procedure. Several experiments are reported, verifying that a FANN with a certain number of hidden nodes has the capability to learn a given polynomial function. The experiments also showed that the proposed algorithm for training a FANN performs accurately
Keywords :
feedforward neural nets; function approximation; learning (artificial intelligence); polynomials; approximating polynomial functions; artificial neural networks; feedforward artificial neural network; hidden nodes; training; Artificial intelligence; Artificial neural networks; Automation; Decision making; Intelligent networks; Intelligent systems; Measurement standards; Nonhomogeneous media; Polynomials; Systems engineering and theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.227074
Filename :
227074
Link To Document :
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