Applications of a Combination of Two Adaptive Filters

Designing a Least Mean Square (LMS) family adaptive algorithm includes solving the wellknown trade-off between the initial convergence speed and the mean-square error in steady state according to the requirements of the application at hands. The trade-off is controlled by the step-size parameter of the algorithm. Large step size leads to a fast initial conver‐ gence but the algorithm also exhibits a large mean-square error in the steady state and in contrary, small step size slows down the convergence but results in a small steady state er‐ ror [9,17]. In several applications it is, however, eligible to have both and hence it would be very desirable to be able to design algorithms that can overcome the named trade-off.