New approach to chaotic signal recognition using nonlinear signal decomposition

机译：非线性信号分解混沌信号识别的新方法

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摘要

Th nonlinear speech signal decomposition based on Vofterra-Wiener functional series is described. The nonlinear filter bank structure is proposed for phoneme recognition solving.

机译：描述了基于Vofterra-Wiener功能系列的非线性语音信号分解。建议非线性滤波器组结构用于音素识别求解。

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《ASME Artificial Neural Networks in Engineering Conference》|1999年||共8页
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Alexander M. Krot; Polina P. Tkachova;
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中图分类 TP18-53;
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1. New Approach to Speech Signal Recognition Using Nonlinear Signal Decomposition by Measuring Wiener Kernels [J] . Alexander M. Krot, Boris A. Goncharov, Polina P. Tkachova International Journal of Smart Engineering System Design . 2002,第4期

机译：测量维纳核的非线性信号分解语音信号识别新方法
2. Nonlinear and adaptive undecimated hierarchical multiresolution analysis for real valued discrete time signals via empirical mode decomposition approach [J] . Kuang Weichao, Yang Zhijing, Ling Bingo Wing-Kuen, Digital Signal Processing . 2015,第Null期

机译：实值离散时间信号的非线性自适应非抽取分层多分辨率分析，通过经验模式分解方法
3. A consideration of the effects by some nonlinearly approximated filters to the optimal demodulation (estimation) of chaotically modulated signals [J] . Matsui Ryuzo, Suzuki Hiroto, Amano Masaaki 電子情報通信学会技術研究報告. ITS. Intelligent Transport Systems Technology . 2000,第696期

机译：考虑一些非线性近似滤波器对混沌调制信号的最佳解调（估计）的影响
4. New approach to chaotic signal recognition using nonlinear signal decomposition [C] . Alexander M. Krot, Polina P. Tkachova ASME Artificial Neural Networks in Engineering Conference . 1999

机译：非线性信号分解混沌信号识别的新方法
5. Estimation and detection of nonlinear/chaotic signals: A Bayesian-based approach. [D] . Bozek-Kuzmicki, Maribeth. 1995

机译：非线性/混沌信号的估计和检测：一种基于贝叶斯的方法。
6. A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes [O] . Javad Rostami, Jingming Chen, Peter W. Tse 2017

机译：平滑经验模态分解的信号处理方法可揭示混凝土管道中高污染导波信号中的隐藏腐蚀痕迹
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）
8. Preprocessing of Seismic Signals by Haar Decomposition and Adaptive Filtering for Pattern Recognition. [R] . brolley, j. e. 1979

机译：基于Haar分解和自适应滤波的地震信号预处理模式识别。

New approach to chaotic signal recognition using nonlinear signal decomposition

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