Applications and Performance Evaluation of Set-Membership Algorithms for Signals in Cn

这项研究集中在复向量值信号集员加权递归最小二乘(SM-WRLS)算法在当前感兴趣的问题中的应用，以及对SM-WRLS在自适应策略、数据选择准则(最优和次优)和计算效率方面的基本工作原理的理解。还研究了最终可用脉动处理实现的高效计算算法。组件任务包括：1)神经网络学习算法；2)语音和图像编码；3)总体发展和性能评估。

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.