DocumentCode
2398085
Title
SIMO Fourier neural networks research
Author
Yang, Xuhua ; Dai, Huaping ; Sun, Yowian
Author_Institution
Inst. of Modern Control Eng., Zhejiang Univ., Hangzhou, China
Volume
2
fYear
2003
fDate
12-15 Oct. 2003
Firstpage
1606
Abstract
This paper proposed the single input multiple outputs (SIMO) Fourier neural networks on the base of Fourier series principle. The SIMO Fourier neural networks turn nonlinear optimization problem into linear optimization problem. So, the SIMO Fourier neural networks highly improve convergence speed and avoid local minima problem. At the same time, under the condition of bounded input and bounded output, the SIMO Fourier neural networks can approximate multiple arbitrary nonlinear mapping relationship at arbitrary accuracy and have good generalization capability.
Keywords
Fourier series; backpropagation; neural nets; optimisation; Fourier series principle; convergence speed; linear optimization; local minima problem; nonlinear mapping relationship; nonlinear optimization problem; single input multiple outputs Fourier neural networks; Control engineering; Convergence; Fourier series; Industrial control; Laboratories; Modems; Neural networks; Optimization methods; Random processes; Sun;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Transportation Systems, 2003. Proceedings. 2003 IEEE
Print_ISBN
0-7803-8125-4
Type
conf
DOI
10.1109/ITSC.2003.1252755
Filename
1252755
Link To Document