DocumentCode :
1818244
Title :
Fourier neural networks
Author :
Silvescu, Adrian
Author_Institution :
Dept. of Comput. Sci., Iowa State Univ., Ames, IA, USA
Volume :
1
fYear :
1999
fDate :
1999
Firstpage :
488
Abstract :
A new kind of neuron model that has a Fourier-like in/out function is introduced. The model is discussed in a general theoretical framework and some completeness theorems are presented. Current experimental results show that the new model outperforms, by a large margin both in representational power and convergence speed, the classical mathematical model of neuron based on weighted sum of inputs filtered by a nonlinear function. The new model is also appealing from a neurophysiological point of view because it produces a more realistic representation by considering the inputs as oscillations
Keywords :
convergence; neural nets; neurophysiology; physiological models; Fourier neural networks; completeness theorems; convergence; neuron model; neurophysiology; nonlinear function; weighted sum; Artificial intelligence; Artificial neural networks; Computational modeling; Computer networks; Computer science; Convergence; Intelligent networks; Mathematical model; Neural networks; Neurons;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.831544
Filename :
831544
Link To Document :
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