DocumentCode
3263347
Title
A new orthogonal neural network
Author
Tseng, Ching-Shiow ; Yang, Shiow-Shung
Author_Institution
Dept. of Mech. Eng., Nat. Central Univ., Chung-Li, Taiwan
Volume
1
fYear
1995
fDate
Nov/Dec 1995
Firstpage
296
Abstract
This paper presents a new neural network based on orthogonal functions. This single-layer neural network may avert the problems of traditional feedforward neural networks such as the determination of the numbers of layers and processing elements, and the initial values of weights. The processing elements of the neural network are composed of the expansion terms of Legendre polynomials. The required number of processing elements is determined according to the desired output accuracy. Because the weights are unique, the training of the weights will converge rapidly. Two experiments are given to demonstrate the performance of the proposed neural network. The results show that the neural network has excellent performance in convergence time and in finding a near-global solution
Keywords
Legendre polynomials; function approximation; neural nets; polynomials; Legendre polynomials; convergence time; expansion terms; near-global solution; orthogonal neural network; single-layer neural network; Approximation error; Backpropagation algorithms; Convergence; Equations; Feedforward neural networks; Mechanical engineering; Multi-layer neural network; Neural networks; Polynomials; Radial basis function networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-2768-3
Type
conf
DOI
10.1109/ICNN.1995.488112
Filename
488112
Link To Document