• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.3
• /
• pp.197-205
• /
• 2014

In applications of adaptive noise control or active noise control, the presence of a transfer function in the secondary path following the adaptive controller and the error path, been shown to generally degrade the performance of the Least Mean Square (LMS) algorithm. Thus, the convergence rate is lowered, the residual power is increased, and the algorithm can become unstable. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used. But these algorithms have slow convergence speed and weakness in the environment that the secondary path and error path are varied. Therefore, I present the new algorithm called the "Bi-directional Filtered-x (BFX) LMS" algorithm with nearly equal computation complexity. Through experimental study, the proposed BFX-LMS algorithm has better convergence speed and better performance than the conventional FX-LMS algorithm, especially when the secondary path or error path is varied and the impulsive disturbance is flow in.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.4
• /
• pp.133-142
• /
• 2014

The least mean square(LMS) algorithm has been popular owing to its simplicity, stability, and availability to implement. But it inherently has a problem of slow convergence speed, and the presence of a transfer function in the secondary path following the adaptive controller and the error path has been shown to generally degrade the stability and the performance of the LMS algorithm in applications of acoustical noise control. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used and the bi-directional Filtered-x LMS(BFXLMS) algorithm is very attractive among them, which increase the convergence speed and the performance of the controller with nearly equivalent computation complexity. In this paper, a mathematical analysis for the BFXLMS algorithm is presented. In terms of view points of time domain, frequency domain, and stochastic domain, the characteristics and stabilities of algorithm is accurately analyzed.