6533b7ddfe1ef96bd1273d9b

RESEARCH PRODUCT

Robust γ-filter using support vector machines

José Luis Rojo-álvarezGustau Camps-vallsEmilio Soria-olivasManel Martínez-ramón

subject

Telecomunicacionesbusiness.industryComputer scienceCognitive NeuroscienceChaoticPattern recognitionComputer Science ApplicationsSupport vector machineFilter designArtificial IntelligenceRobustness (computer science)3325 Tecnología de las TelecomunicacionesArtificial intelligencebusiness

description

This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the g-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the g-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM g-filter. Teoría de la Señal y Comunicaciones

yearjournalcountryeditionlanguage
2009-07-23Neurocomputing
https://doi.org/10.1016/j.neucom.2004.07.003