A Novel Neural-Fuzzy Method to Search the Optimal Step Size for NLMS Beamforming

Walter Orozco-Tupacyupanqui, Mariko Nakano-Miyatake, Hector Perez-Meana

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

7 Citas (Scopus)

Resumen

© 2003-2012 IEEE. This paper presents a novel algorithm based on neural networks and fuzzy logic to generate membership functions and search an approximation of the optimal step-size for Normalized Least Mean Squares (NLMS) beamforming systems. The proposed method makes a new error curve, Error Ensemble Learning (EEL), based on the final estimated value of the adaptive algorithm's mean-square-error. A fuzzy clustering method individually assigns membership values to each EEL curve coordinates. This information is fed into a neural network to generate membership functions for a fuzzy inference system. The final estimation of the optimal step-size is obtained using a group of Mamdani linguistic propositions and the centroid defuzzification method. Simulation results show that a useful approximation of the optimal step-size is obtained for different interference conditions; the evaluation results also show that a higher directivity is achieved in the radiation beam pattern.
Idioma originalInglés
Páginas (desde-hasta)402-408
Número de páginas7
PublicaciónIEEE Latin America Transactions
DOI
EstadoPublicada - 1 feb. 2015

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