Copyright Information: This publication has been posted here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

When applying certain adaptive algorithms, such as the popular LMS
and NLMS algorithms, in adaptive noise canceling or adaptive equalization
scenarios where a strong narrowband interference component is present, these
algorithms exhibit nonlinear dynamic behaviors. The latter is expressed in filter
weights – that are generally expected to converge to constants – exhibiting a
dynamic component. Furthermore, the performance of these adaptive filters with
dynamic weight behavior can exceed the performance of any filter of the same
structure in which those weights are fixed. In adaptive noise canceling
applications, the dynamic component of the weights can be unmistakably large. In
adaptive equalization scenarios, however, the dynamic aspect of the weight
behavior can easily be mistaken for low level noise. These various findings will be
illustrated.

• Adaptive Filtering
• Nonlinear Effects
• Non-Wiener Effects
• Dynamic Weight Behavior