Power spectral density analysis for nonlinear systems based on Volterra series

来源 :应用数学和力学(英文版) | 被引量 : 0次 | 上传用户:wwkuan
下载到本地 , 更方便阅读
声明 : 本文档内容版权归属内容提供方 , 如果您对本文有版权争议 , 可与客服联系进行内容授权或下架
论文部分内容阅读
A consequence of nonlinearities is a multi-harmonic response via a mono-harmonic excitation.A similar phenomenon also exists in random vibration.The power spectral density (PSD) analysis of random vibration for nonlinear systems is studied in this paper.The analytical formulation of output PSD subject to the zero-mean Gaussian random load is deduced by using the Volterra series expansion and the conception of generalized frequency response function (GFRF).For a class of nonlinear systems,the growing exponential method is used to determine the first 3rd-order GFRFs.The pro-posed approach is used to achieve the nonlinear system\'s output PSD under a narrow-band stationary random input.The relationship between the peak of PSD and the parame-ters of the nonlinear system is discussed.By using the proposed method,the nonlinear characteristics of multi-band output via single-band input can be well predicted.The results reveal that changing nonlinear system parameters gives a one-of-a-kind change of the system\'s output PSD.This paper provides a method for the research of random vibration prediction and control in real-world nonlinear systems.
其他文献
日冕物质抛射(Coronal Mass Ejection,CME)是一种强烈的太阳爆发现象,对空间天气和人类生活有巨大的影响,因此,日冕物质抛射检测对预报日冕物质抛射、保障人类的生产生活安全具有重要意义.现有的日冕物质抛射检测多采用人为定义特征和界定阈值等方法.由于人为定义特征不能准确表征日冕物质抛射且具有普适性的阈值难于选择,现有的方法对日冕物质抛射的检测效果有待提高.提出一种基于Faster R-CNN(Faster Region-based Convolutional Neural Networks
Spiral springs have a wide range of applications in various fields.As a result of the complexity of friction,few theoretical analyses of spring belts under static loading have been carried out.Considering the piecewise smooth property of the whole contact
Toward accurately simulating both hardening and softening effects for metals up to failure,a new finite strain elastoplastic J2-flow model is proposed with the yield strength therein as a function of the plastic work in the explicit form.With no need to i
针对高维核矩阵构造的极化码中为提升纠错性能而造成的复杂度增加的问题,提出了基于3×3高维核矩阵终止极化码的构造方案.首先筛选出极化率最高的核矩阵G531构造终止极化码,并在二进制擦除信道上证明了在不影响纠错性能的前提下终止极化码能够降低编译码计算复杂度,同时推导出终止极化码的复杂度降低比的上下界.仿真表明,终止极化码复杂度降低比与二进制擦除信道(binary erasure channel,BEC)的擦除概率有关,在擦除概率为0.5左右时,复杂度降低比最小,且目标误帧率(frame error rate,
The concept of local resonance phononic crystals proposed in recent years provides a new chance for theoretical and technical breakthroughs in the structural vi-bration reduction.In this paper,a novel sandwich-like plate model with local resonator to acqu
The explicit expression of Eshelby tensors for one-dimensional (1D) hexag-onal quasicrystal composites is presented by using Green\'s function method.The closed forms of Eshelby tensors in the special cases of spheroid,elliptic cylinder,ribbon-like,penn
梳理了近年来神经网络水印技术的发展脉络,将主流方法大致归为白盒水印、黑盒水印、无盒水印和脆弱水印.综述了神经网络水印的评价指标和上述4种不同类型的神经网络水印技术,探讨了现有神经网络水印方案的优缺点,并对未来的发展趋势进行了展望.
在方面信息情感分类中针对使用循环神经网络编码长距离文本的信息丢失问题,以及使用注意力机制提取情感信息时倾向于关注高频信息偏置问题,提出一种多模特征融合的方面信息情感分类方法,区分单点、多点以及局部三类不同表达模式的情感信息,通过对三类情感信息有侧重的关注、提取与融合,实现各类特征间相互确认与纠错,降低信息丢失与关注偏置问题,达到增强复杂情感表达模式下的方面信息情感分类能力的目的 .实验结果表明,使用所提出的方法对三类情感信息进行提取与融合,可以使方面信息情感分类任务在准确率和F1值指标上得到进一步提升.
The unusual properties of quasicrystals (QCs) have attracted tremendous attention from researchers.In this paper,a semi-analytical solution is presented for the static response of a functionally graded (FG) multilayered two-dimensional (2D) decago-nal QC
In this paper,we propose general strain-and stress-driven two-phase lo-cal/nonlocal piezoelectric integral models,which can distinguish the difference of non-local effects on the elastic and piezoelectric behaviors of nanostructures.The nonlocal piezoelec