A pulsed neural network capable of universal approximation

Author

Cotter, Neil E. ; Mian, Omar N.

Author_Institution

Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA

Volume

Issue

fYear

1992

fDate

3/1/1992 12:00:00 AM

Firstpage

308

Lastpage

314

Abstract

The authors describe a pulsed network version of the cerebellar model articulation controller (CMAC), popularized by Albus (1981). The network produces output pulses whose times of occurrence are a function of input pulse intervals. Within limits imposed by causality conditions, this function can approximate any bounded measurable function on a compact domain. Simulation results demonstrate the viability of training the network with a least mean square algorithm

Keywords

function approximation; least squares approximations; neural nets; physiological models; bounded measurable function; causality conditions; cerebellar model articulation controller; function approximation; least mean square algorithm; physiological models; pulsed neural network; universal approximation; Biological information theory; Biological neural networks; Biological system modeling; Least mean square algorithms; Least squares approximation; Mathematical model; Nervous system; Neural networks; Neurons; Timing;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.125872

Filename

125872

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=856692