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 :
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