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

This paper proposes a new application based on fuzzy logic to search the optimal Step Size of the Normalized Least Mean Square (NLMS) algorithm for beamforming systems. The searching of the step size depends on the fuzzy inference results of an estimation of the final value of the cost function (JE) instead of using its instantaneous value. The update of the step-size is performed outside of the adaptive algorithm and given it feedback by the fuzzy inference system; therefore the step-size is still fixed for the NLMS algorithm but variable for the complete searching scheme. Simulation experimental results show that a useful approximation of the optimal step-size can be obtained for different conditions of signal-to-noise plus interference ratios (SINR) and the minimization of the mean square error for the adaptive beamforming algorithm is also achieved.

[1]  Hafizal Mohamad,et al.  Block-based fuzzy step size nlms algorithms for subband adaptive channel equalisation , 2009 .

[2]  Hung-Yan Gu,et al.  A new speech enhancing scheme combining NLMS, fuzzy logic and Kalman filtering , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[3]  Junghsi Lee,et al.  A New Variable Step-Size NLMS Algorithm and Its Performance Analysis , 2012, IEEE Transactions on Signal Processing.

[4]  R.C. Johnson,et al.  Introduction to adaptive arrays , 1982, Proceedings of the IEEE.

[5]  InfanteJ. C. García,et al.  Description of adaptive fuzzy filtering using the DSP TMS320C6713 , 2009, 2009 52nd IEEE International Midwest Symposium on Circuits and Systems.

[6]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[7]  Paulo S. R. Diniz.  Errata: Adaptive Filtering Algorithms and Practical Implementation , 2013 .

[8]  Paulo S. R. Diniz,et al.  Adaptive Filtering: Algorithms and Practical Implementation , 1997 .

[9]  E. H. Mandami.  Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis , 1977 .

[10]  Timothy J. Ross,et al.  Fuzzy Logic with Engineering Applications: Ross/Fuzzy Logic with Engineering Applications , 2010 .

[11]  Xiaoli Xi,et al.  A New Variable Step-Size NLMS Adaptive Filtering Algorithm , 2013, 2013 International Conference on Information Technology and Applications.

[12]  Lester A. Gerhardt,et al.  Adaptive digital filters , 1973, CDC 1973.

[13]  K. Axhausen,et al.  The simple exponential smoothing learning model , 1994 .

[14]  Laurence Tianruo Yang,et al.  Fuzzy Logic with Engineering Applications , 1999 .

[15]  Yi Yu,et al.  Novel adaptive VSS-NLMS algorithm for system identification , 2013, 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP).

[16]  Zoran Banjac,et al.  Adaptive Digital Filters , 2013 .