This research is centered on the application of complex vector valued signal set-membership weighted recursive least squares (SM- WRLS) algorithms to problems of current interest and the understanding of the basic operational principles of SM-WRLS with regard to adaptation strategies, data selection criteria (optimal and suboptimal) and computational efficiency. Efficient computational algorithms which can ultimately be implemented using systolic processing are also investigated. Component tasks include: 1) neural network learning algorithms; 2) speech and image coding, and 3) general developments and performance evaluation.
