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 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.

A Hybrid Algorithm for Adaptive Neuro-controllers

“…Backpropagation neural network (BPN) is one of the most well-known and common methods which is used to minimize the error of possible objective functions (Oztekin and Ozgan, 2012;Chen and Gu, 2020). Although this approach seems suitable for control system identification problems, however, it has some drawbacks in real-time applications because of long training time and slow performance issues (Orozco-Tupacyupanqui et al, 2016). Another well-known approach in literature for minimizing the error is the LMS algorithm.…”