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

信号集合隶属算法在CN中的应用及性能评估

• 批准号: 9016734
• 负责人: John Deller
• 金额: $ 13.59万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-02-15 至 1993-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9016734&HistoricalAwards=false
• 关键词: Applications; Performance; Evaluation; Set; Membership

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.

本研究的重点是将复向量值信号集隶属度加权递归最小二乘（SM- WRLS）算法应用于当前关注的问题，并了解SM-WRLS在自适应策略、数据选择标准（最优和次优）和计算效率方面的基本操作原理。有效的计算算法，最终可以实现使用收缩处理也进行了研究。组件任务包括：1)神经网络学习算法；2)语音和图像编码，以及3)一般发展和性能评估。