Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/843

 Title: Fourier Neural Networks: An Approach with Sinusoidal Activation Functions Authors: Mingo, LuisAslanyan, LevonCastellanos, JuanDíaz, MiguelRiazanov, Vladimir Keywords: Neural NetworksSinusoidal Activation Functions Issue Date: 2004 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: This paper presents some ideas about a new neural network architecture that can be compared to a Fourier analysis when dealing periodic signals. Such architecture is based on sinusoidal activation functions with an axo-axonic architecture [1]. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks [2] in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties [3] even with lineal activation functions. Description: * Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03. URI: http://hdl.handle.net/10525/843 ISSN: 1313-0463 Appears in Collections: Volume 11 Number 1

Files in This Item:

File Description SizeFormat