On-line adaptive algorithms in non-stationary environments using a modified conjugate gradient approach

In this paper we propose novel computationally efficient schemas for a large class of online adaptive algorithms with variable self-adaptive learning rates. The learning rate is adjusted automatically providing relatively fast convergence at early stages of adaptation while ensuring small final misadjustment for cases of stationary environments. For nonstationary environments, the algorithms proposed have good tracking ability and quick adaptation to new conditions. Their validity and efficiency are illustrated for a nonstationary blind separation problem.