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

Authors : Walter OROZCO-TUPACYUPANQUI, Mariko NAKANO-MIYATAKE, Hector Perez MEANA

Pages : 4322-4338

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Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :In this paper, a novel algorithm based on fuzzy logic and neural networks is proposed to find an approximation of the optimal step size $\mu $ for least-mean-squares insert ignore into journalissuearticles values(LMS); adaptive beamforming systems. A new error ensemble learning insert ignore into journalissuearticles values(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 mean-square-error is observed during the training process of the adaptive array beamforming system, and a higher directivity is achieved in the radiation beam patterns.
Keywords : Adaptive filtering, adaptive beamforming, neural fuzzy systems, least mean square algorithm, membership functions