.png){kind=icon}
返回
Wavelet basis expansion-based Volterra kernel function identification through multilevel excitations
.png){kind=icon}
PRE
AI
DOI：10.1007/s11071-013-1182-3.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Volterra series is a powerful mathematical tool for nonlinear system analysis, which extends the convolution integral for linear system to nonlinear system. There is a wide range of nonlinear engineering systems and structures which can be modeled as Volterra series. One question involved in modeling a functional relationship between the input and output of a system using Volterra series is to identify the Volterra kernel functions. In this article, a wavelet balance method-based approach is proposed to identify the Volterra kernel functions from observations of the in- and outgoing signals. The basic routine of the approach is that, from the system outputs under multilevel excitations, the Volterra series outputs of different orders are first estimated with the wavelet balance method, and then the Volterra kernel functions of different orders are separately estimated through their corresponding Volterra series outputs by expanding them with four-order B-spline wavelet on the interval. The simulation studies verify the effectiveness of the proposed Volterra kernel identification method.
Keyword:
Volterra series
B-spline wavelet on the interval (BSWI)
Wavelet balance method
Kernel function identification
Nonlinear system
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
1.4W
被引数:
4.1W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏Nonlinear behaviour of structures using the Volterra series - Signal processing and testing methods
PREAI
分享收藏
查看更多内容