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

作者：Orozco Tupacyupanqui Walter; Nakano Miyatake Mariko; Perez Meana Hector^*

来源：Turkish Journal of Electrical Engineering and Computer Sciences, 2016, 24(5): 4322-4338.

DOI：10.3906/elk-1410-76

• Adaptive filtering
• adaptive beamforming
• neural-fuzzy systems
• least-mean-square algorithm
• membership functions

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.

• 出版日期2016