简洁的Volterra系统辨识
Parsimonious Volterra System Identification
AI总结：
本文提出了一种用于稀疏Volterra系统辨识的算法，针对具有无限脉冲响应的系统，通过最小化指数项的数量来寻找最简单的非线性Volterra模型，该模型能与先验信息和收集的数据兼容，并能处理碎片化数据和测量噪声。
摘要：
在本文中，我们旨在开发用于稀疏Volterra系统辨识的算法，当被辨识系统具有无限脉冲响应时。假设脉冲响应可以表示为指数的和，并且给定输入输出数据，研究目标是找到一个与先验信息和收集数据相容的最简单的非线性Volterra模型。所谓最简单，指的是脉冲响应具有最少数量的指数项。所提供的算法能够处理碎片化数据和测量噪声。论文最后的学术示例展示了所提出方法的有效性。
英文摘要：
In this short paper, we aim at developing algorithms for sparse Volterra system identification when the system to be identified has infinite impulse response. Assuming that the impulse response is represented as a sum of exponentials and given input-output data, the problem of interest is to find the "simplest" nonlinear Volterra model which is compatible with the a priori information and the collected data. By simplest, we mean the model whose impulse response has the least number of exponentials. The algorithms provided are able to handle both fragmented data and measurement noise. Academic examples at the end of paper show the efficacy of proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
