A new fuzzy logic application to search the optimal step size for NLMS beamforming

Orozco-Tupacyupanqui, W.; Pérez-Meana, Héctor; Nakano-Miyatake, Mariko

doi:10.1109/tsp.2015.7296416

Cited by 3 publications

(2 citation statements)

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“…With the lower fitting model, the waves were eliminated, and the main characteristics such as the center of the peak and the peak width were maintained. Five functions were selected for the EELC amplitude and the step size µ according to the following fuzzy IF-THEN rules [18] The tracking performance of the proposed algorithm is illustrated in Figures 11 and 12 for a SINR condition of 2 dB. Similar results were observed for the other SINR conditions.…”

Section: Lms Beamformingmentioning

confidence: 53%

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A new step-size searching algorithm based on fuzzy logic and neural networks for LMS adaptive beamforming systems

Orozco-Tupacyupanqui¹,

Nakano-Miyatake²,

Pérez-Meana³

2016

Turk J Elec Eng & Comp Sci

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In this paper, a novel algorithm based on fuzzy logic and neural networks is proposed to find an approximation of the optimal step size µ for least-mean-squares (LMS) adaptive beamforming systems. A new error ensemble learning (EEL) curve is generated based on the final prediction value of the ensemble-average learning curve of the LMS adaptive algorithm. This information is classified and fed into a back propagation neural network, which automatically generates membership functions for a fuzzy inference system. An estimate of the optimal step size is obtained using a group of linguistic rules and the corresponding defuzzification method. Computer simulations show that a useful approximation of the optimal step size is obtained under different signal-to-noise plus interference ratios. The results are also compared with data obtained from a statistical analysis performed on the EEL curve. As a result of this application, a better meansquare-error is observed during the training process of the adaptive array beamforming system, and a higher directivity is achieved in the radiation beam patterns.

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Section: Lms Beamformingmentioning

confidence: 53%

Section: Lms Beamformingmentioning

confidence: 99%