Convergence and Robustness Studies of Sign-Sign Adaptive Identifiers

符号自适应标识符的收敛性和鲁棒性研究

• 批准号: 8618240
• 负责人: Soura Dasgupta
• 金额: $ 6万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-06-15 至 1990-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8618240&HistoricalAwards=false
• 关键词: Convergence; Robustness; Studies; Sign; Adaptive

The sign-sign adaptive identifier is a computationally efficient variant of the standard identifier, in which the regressor and prediction error multiplicands of the update term of the LMS are replaced by their respective signum functions. Despite its wide use in a host of communications and signal processing applications, theoretical investigations into its properties are very few. Accordingly, the proposed project will conduct a theoretical study of convergence and robustness properties of this algorithm. The proposal outlines a number of performance differences between the sign-sign and the standard LMS. It emphasizes certain robustness enhancements enjoyed by the sign-sign relative to the standard LMS. It also demonstrates potential instability mechanisms within the sign-sign and highlights the need for developing excitation conditions for arresting such mechanisms and for ensuring the convergence of parameter estimates to their desired values. The non-gradient based nature of this algorithm, together with its intrinsic nonlinearity are expected to make its convergence and robustness analysis substantially different from those for traditional gradient based algorithms. The expected strategies for conducting such analyses are outlined, as also are the potential theoretical and practical benefits which this study is expected to carry. This research will attempt to derive a well founded theoretical basis for the sign-sign adaptive identifier which is an algorithm used to identity unknown signals. It is expected that such a theoretical basis will allow variations of the algorithm which can be employed in both signal processing and adaptive control.

符号-符号自适应识别器是一种计算效率高的 标准标识符的变体，其中回归量和 LMS的更新项的预测误差乘数为 由它们各自的符号函数代替。 尽管它被广泛使用， 一系列通信和信号处理应用，理论 对其性质的研究很少。 因此 拟议的项目将进行理论研究的收敛和 该算法的鲁棒性。 该提案概述了一些 和标准LMS之间的性能差异。 它强调了符号-符号所享有的某些鲁棒性增强 相对于标准的LMS。 它也展示了潜力 不稳定机制内的标志，标志，并强调需要 开发激发条件，用于阻止这种机制， 确保参数估计收敛到它们的期望值。 该算法的非梯度性质，连同其 内在非线性预期使其收敛， 鲁棒性分析与传统的 梯度算法 开展此类活动的预期战略 分析概述，也是潜在的理论和 这一研究预期带来的实际好处。 本研究将试图得出一个有根据的理论 符号-符号自适应识别器的基础， 识别未知信号 预计这样的理论 基础将允许算法的变化，其可以用于 信号处理和自适应控制。