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

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 (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 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.