Domain of attraction on adaptive feature extraction of nonstationary processes

The authors review briefly the procedure relating to the convergence analysis of a learning algorithm for adaptive feature extraction. They then address the issue of identification of a nontrivial domain of attraction for the learning system. The problem is important because such an identification is not only powerful for choosing initial settings of the system, but also holds one of the keys to the quantitative analysis of its adaptivity in a nonstationary environment. The primary results concerning convergence analysis of this algorithm are briefly reviewed.<>