Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS)

The Filtered-x LMS Algorithm

A Filtered Reference – Filtered Error LMS Algorithm

Adaptive filters updated by the least mean square (LMS) algorithm are succesfully implemented in digital control systems. They are utilized for both plant identification and control purposes. In control appications (e.g. in active noise control) the output of the adaptive filter drives the plant input, and the error signal is derived only at the output of the plant. In such cases the filtered r...

Second moment analysis of the filtered-X LMS algorithm

Active noise and vibration control (ANC) has become an important application area for adaptive filters. The most popular adaptive algorithm used for ANC is the Filtered-X LMS (FXLMS) algorithm [1,2]. This algorithm is a modification of the well known LMS algorithm. The reference signal is filtered so as to compensate for filters inherent in the electro-acoustic adaptation loop. These additional...

Eigenvalue equalization filtered-x algorithm for the multichannel active noise control of stationary and nonstationary signals.

The FXLMS algorithm, which is extensively used in active noise control, exhibits frequency dependent convergence behavior. This leads to degraded performance for time-varying and multiple frequency signals. A new algorithm called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) has been developed to overcome this limitation without increasing the computational burden of the ...

Mean weight behavior of the Filtered-X LMS algorithm

This paper presents a stochastic analysis of the Filtered-X LMS algorithm. The mean weight vector recursion is derived for slow adaptation and for a white reference signal without use of independence theory. The Wiener solution is determined explicitly as a function of the input statistics and the impulse responses of the primary and secondary signal paths. It is shown that the steady-state mea...