Feed-forward neural networks using hermite polynomial activation functions

Rigatos, GG; Tzafestas, SG

dc.contributor.author	Rigatos, GG	en
dc.contributor.author	Tzafestas, SG	en
dc.date.accessioned	2014-03-01T02:44:03Z
dc.date.available	2014-03-01T02:44:03Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31641
dc.subject	Diffusion Equation	en
dc.subject	Feed Forward Neural Network	en
dc.subject	Harmonic Oscillator	en
dc.subject	Hermite Polynomial	en
dc.subject	Image Processing	en
dc.subject	Nonparametric Estimation	en
dc.subject	System Modelling	en
dc.subject	Activation Function	en
dc.subject	Fourier Transform	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Eigenvalues and eigenfunctions	en
dc.subject.other	Fourier transforms	en
dc.subject.other	Functions	en
dc.subject.other	Oscillators (electronic)	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Polynomials	en
dc.subject.other	Hermite basis functions	en
dc.subject.other	Particle-wave nature	en
dc.subject.other	Polynomial activation functions	en
dc.subject.other	System modeling	en
dc.subject.other	Feedforward neural networks	en
dc.title	Feed-forward neural networks using hermite polynomial activation functions	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11752912_33	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11752912_33	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	In this paper feed-forward neural networks are introduced where hidden units employ orthogonal Herrnite polynomials for their activation functions. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, and (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger's diffusion equation. The proposed neural networks demonstrate the particle-wave nature of information and can be used in nonparametric estimation. Possible applications of neural networks with Hermite basis functions include system modelling and image processing. © Springer-Vorlag Berlin Hoidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11752912_33	en
dc.identifier.isi	ISI:000238053100031	en
dc.identifier.volume	3955 LNAI	en
dc.identifier.spage	323	en
dc.identifier.epage	333	en