Nuevo Algoritmo de Búsqueda del Tamaño de Paso Óptimo de Arreglos de Antenas Inteligentes NLMS
(A Novel Neural-Fuzzy Method to Search the Optimal Step Size for NLMS Beamforming)
Walter Orozco-Tupacyupanqui (firstname.lastname@example.org), Mariko Nakano-Miyatake (email@example.com), Héctor Pérez-Meana (firstname.lastname@example.org)
Instituto Politécnico Nacional
This paper appears in: Revista IEEE América Latina
Publication Date: Feb. 2015
Volume: 13, Issue: 2
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.
Adaptive filters, Beamforming, Fuzzy logic, Neural networks, NLMS algorithm
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