Comments on 'a frequency domain based numeric-analytical method for non-linear dynamical systems'

R. Van Dooren

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Comments on 'a frequency domain based numeric-analytical method for non-linear dynamical systems'

机译：关于“基于非线性非线性动力系统的频域数值分析方法”的评论

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《Journal of Sound and Vibration》 |1999年第4期|共7页
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R. Van Dooren;
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中图分类声学;
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1. Comments on "a frequency domain based numeric-analytical method for non-linear dynamical systems" [J] . R. Van Dooren Journal of Sound and Vibration . 1999,第4期

机译：关于“基于非线性非线性动力系统的频域数值分析方法”的评论
2. Frequency domain ARX model and multi-harmonic FRF estimators for non-linear dynamic systems [J] . Adams DE. Journal of Sound and Vibration . 2002,第5期

机译：非线性动力系统的频域ARX模型和多谐波FRF估计器
3. Comments on 'Frequency-domain conditions for the robust stability of linear and nonlinear dynamical systems' by S. Dasgupta et al [J] . Boese F.G. IEEE Transactions on Circuits and Systems. 1 . 1993,第4期

机译：S. Dasgupta等人对“线性和非线性动力系统鲁棒稳定性的频域条件”的评论
4. Dynamic Mesh Deformation with Radial Basis Functions for the Non-Linear Frequency Domain Method [C] . Pierre-Olivier Tardif, Siva K. Nadarajah AIAA SciTech forum;AIAA Aerospace Sciences Meeting . 2015

机译：非线性频域方法的径向基函数动态网格变形
5. The application of non-linear frequency domain methods to the Euler and Navier-Stokes equations. [D] . McMullen, Matthew Scott. 2003

机译：非线性频域方法在Euler和Navier-Stokes方程中的应用。
6. Study on non-linear bistable dynamics model based EEG signal discrimination analysis method [O] . Xiaoguo Ying, Han Lin, Guohua Hui 2015

机译：基于非线性双稳态动力学模型的脑电信号判别分析方法研究
7. EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed TarekShahriar Abstract—An EMD-chaos based approach is proposed todiscriminate EEG signals corresponding to healthy persons,and epileptic patients during seizure-free intervals and seizureattacks. An electroencephalogram (EEG) is first empiricallydecomposed to intrinsic mode functions (IMFs). The nonlineardynamics of these IMFs are quantified in terms of the largestLyapunov exponent (LLE) and correlation dimension (CD).This chaotic analysis in EMD domain is applied to a large groupof EEG signals corresponding to healthy persons as well asepileptic patients (both with and without seizure attacks). It isshown that the values of the obtained LLE and CD exhibitfeatures by which EEG for seizure attacks can be clearlydistinguished from other EEG signals in the EMD domain.Thus, the proposed approach may aid researchers in developingeffective techniques to predict seizure activities. Index Terms—Electroencephalogram (EEG), empiricalmode decomposition (EMD), largest Lyapunov exponent (LLE),correlation dimension (CD), epileptic seizures. The Authors are with the Electrical and Electronic EngineeringDepartment, Bangladesh University of Engineering and Technology,Dhaka-1000, Bangladesh (e-mail: imamul@eee.buet.ac.bd) PDF Cite: S. M. Shafiul Alam,S. M. Shafiul Alam,Aurangozeb, and Syed Tarek Shahriar, "EEG Signal Discrimination using Non-linear Dynamics in the EMD Domain," International Journal of Computer and Electrical Engineering vol. 4, no. 3, pp. 326-330, 2012. PREVIOUS PAPER Perception of Emotions Using Constructive Learningthrough Speech NEXT PAPER Physical Layer Impairments Aware OVPN Connection Selection Mechanisms Copyright © 2008-2013. International Association of Computer Science and Information Technology Press (IACSIT Press) [O] . S. M. Shafiul Alam, Syed TarekShahriar 2012

机译：EEG信号在EMD域S. S. Shafiul Alam，S中的非线性动力学使用非线性动力学。 M. Shafiul Alam，Aurangozeb和Syed Tarekshahriar摘要 - 基于EMD Chaos的方法，提出了对应于健康人的EEG信号，癫痫发作期间的癫痫患者和Seizureattacks。脑电图（EEG）首先被凭经上分解为内在模式功能（IMF）。这些IMF的非线性动力学在最大范围的指数（LLE）和相关尺寸（CD）方面是量化的。本域中的混沌分析应用于与健康人相对应的大型脑电图（Asepileptic患者）（两者都有癫痫发作）。因此，所获得的LLE和CD表展的价值可以从EMD领域的其他EEG信号中清晰地区分脑电图的表达展示。本拟议的方法可以帮助研究人员以预测癫痫发作的癫痫发作技术。索引术语 - 脑电图（EEG），仿真态分解（EMD），最大的Lyapunov指数（LLE），相关维度（CD），癫痫发作。作者与电气电子和电子工程公司，孟加拉国工程和技术大学，孟加拉国达卡 - 1000（电子邮件：imamul@eee.buet.ac.bd）pdf cite：s. m. shafiul Alam，s。 M. Shafiul Alam，Aurangozeb和Syed Tarek Shahriar，“EEG信号歧视在EMD领域的非线性动态，”计算机电气工程卷国际杂志。 4，不。 3，pp。326-330,2012，上一篇论文对情绪的看法，使用建设性的学习言论下一篇论文物理层障碍意识到OVPN连接选择机制版权所有©2008-2013。国际计算机科学与信息技术协会出版社（IACSIT Press）

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