BUILDING A NEURO-FUZZY SYSTEM TO EFFICIENTLY FORECAST CHAOTIC TIME-SERIES

Authors
STUDER L MASULLI F
Citation
L. Studer et F. Masulli, BUILDING A NEURO-FUZZY SYSTEM TO EFFICIENTLY FORECAST CHAOTIC TIME-SERIES, Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment, 389(1-2), 1997, pp. 264-267
Citations number
8
Categorie Soggetti
Nuclear Sciences & Tecnology","Physics, Particles & Fields","Instument & Instrumentation",Spectroscopy
Journal title
Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment → ACNP
ISSN journal
01689002
Volume
389
Issue
1-2
Year of publication
1997
Pages
264 - 267
Database
ISI
SICI code
0168-9002(1997)389:1-2<264:BANSTE>2.0.ZU;2-X
Abstract
In this paper we show which elements have to be extracted from a chaot ic time series in order to define the architecture of a forecaster. Th e forecaster chosen here is a Neuro-Fuzzy System (NFS). This NFS is tr ained by a supervised gradient descent algorithm. The NFS is made of a layer of singleton inputs, a hidden layer of Gaussian membership func tions and one output unit. Product is used for rule inference and sum for rule composition. Output is given by a height defuzzifier. Test ca ses based on Mackey-Glass time series are presented.