Non-stationary Learning with Multi-layer of Adaptation

Time-varying system behavior learning is crucial in the current landscape of machine learning, where statistical behavior often lacks precision. This approach effectively models many real-world scenarios characterized by their time-varying non-stationary nature. The focus has shifted towards adaptive systems for these applications, as they demonstrate superior performance compared to traditional fixed-weight systems that have been in use for a longer period. The introduction of multi-layer adaptation has further enhanced the effectiveness of signal processing in this context. A single layer of adaptation utilizing a recursive or steepest-gradient filter is insufficient to ensure both rapid convergence and optimal error fidelity during the learning process, particularly in unknown time-varying environments. Therefore, a multi-layer adaptation approach that combines least mean squares (LMS) and recursive least squares (RLS) algorithms has been proposed, incorporating a mixing parameter [5], [6], [7] that plays a pivotal role in the learning process. This multi-layer adaptation has been shown to significantly improve learning capabilities, achieving approximately 4 to 5 dB better noise fidelity compared to existing single-layer adaptation methods.