语音信号序列的Volterra预测模型

对给定的英语音素、单词和语句进行了采集并完成预处理. 分别应用互信息法和Cao 氏法确定了实际采集的语音信号序列的延迟时间和嵌入维数, 以完成语音序列的相空间重构. 通过计算实际采集的语音信号序列的最大Lyapunov指数, 完成了语音信号的混沌特性识别, 判定其具有混沌特性. 引入Volterra级数, 提出了一种具有显式结构的语音信号非线性预测模型. 为克服最小均方误差算法在Volterra模型系数更新时固有的缺点, 在最小二乘法基础上, 应用基于后验误差假设的可变收敛因子技术, 构建了一种基于Davidon-Fletcher-Powell算法的二阶Volterra 模型(DFPSOVF), 并将其应用于具有混沌特性的语音信号序列预测. 仿真结果表明: DFPSOVF非线性预测模型对于单帧和多帧语音信号均具有更好的预测精度, 优于线性预测模型, 并且能够很好地反映语音序列变化的趋势和规律, 完全可以满足语音预测的要求; 可以根据语音信号序列的嵌入维数选取预测模型的记忆长度. 所提出模型可以为语音信号重构和压缩编码开辟一条新途径, 以改善语音信号处理方法的复杂度和处理效果.     

Simulation of nonlinear bridge aerodynamics: A sparse third-order Volterra model

A sparse third-order Volterra model is utilized to simulate nonlinear bridge aerodynamics and attendant response under turbulent fluctuations. The Volterra model is pruned based on aerodynamic considerations, which significantly reduces computational effort needed for nonlinear analysis. The first-, second- and third-order Volterra kernels are identified using least-squares with the input–output pairs obtained from a numerical simulation and a wind-tunnel experiment. Both studies involving a simulation and an experiment show that the proposed sparse third-order Volterra model can adequately simulate nonlinear bridge aerodynamics with high fidelity. Finally, the robustness of the sparse Volterra model is verified by comparing it to an upgraded Volterra scheme.     

Identification of a parametric loudspeaker system using an adaptive Volterra filter

Due to the parametric acoustic array effect in air, the input audible signal of a parametric loudspeaker system can be reproduced with high directivity at the target region. However, the reproduced audible signal suffers from harmonic distortion, which is the by-product of nonlinear interaction between the primary waves. In order to investigate this inherent nonlinear phenomenon, a nonlinear system identification model is developed based on an adaptive Volterra filter. Unlike the conventional loudspeaker, the nonlinear characteristic of a parametric loudspeaker system is dependent on several primary parameters in nonlinear acoustics, which include the initial pressure of the primary waves, the observing distance and angle, as well as ambient temperature and relative humidity. By using a truncated Volterra series up to the 2nd-order kernel, numerical simulations are conducted to develop a system model with one group of parameters and examine the quadratic nonlinear intensity for different parameters’ settings. Experimental measurements, which take into account of emitter’s response, are carried out to verify the modeling result and evaluate the model performance. Based on the Volterra system model, the sound pressure level and the harmonic distortion can be accurately predicted.     

A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction

A method of modifying the architecture of fractional least mean square （FLMS） algorithm is presented to work with nonlinear time series prediction. Here we incorporate an adjustable gain parameter in the weight adaptation equation of the original FLMS algorithm and absorb the gamma function in the fractional step size parameter. This approach provides an interesting achievement in the performance of the filter in terms of handling the nonlinear problems with less computational burden by avoiding the evaluation of complex gamma function. We call this new algorithm as the modified fractional least mean square （MFLMS） algorithm. The predictive performance for the nonlinear Mackey glass chaotic time series is observed and evaluated using the classical LMS, FLMS, kernel LMS, and proposed MFLMS adaptive filters. The simulation results for the time series with and without noise confirm the superiority and improvement in the prediction capability of the proposed MFLMS predictor over its counterparts.     

用于混沌时间序列自适应预测的一种少参数二阶Volterra滤波器

研究了二阶Volterra滤波器的一种乘积耦合近似实现结构及其非线性NLMS自适应算法,并用这种少参数二阶Volterra滤波器(RPSOVF)研究了一些混沌信号的非线性自适应预测性能.仿真研究结果表明:所给出的非线性NLMS自适应算法能够保证这种RPSOVF的稳定性和收敛性,且RPSOVF用这种非线性NLMS自适应算法能够自适应预测一些混沌时间序列. 关键词： 混沌 非线性自适应预测 Volterra滤波器 非线性NLMS自适应算法     

Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction

This paper presents an adaptive step-size modified fractional least mean square （AMFLMS） algorithm to deal with a nonlinear time series prediction. Here we incorporate adaptive gain parameters in the weight adaptation equation of the original MFLMS algorithm and also introduce a mechanism to adjust the order of the fractional derivative adaptively through a gradient-based approach. This approach permits an interesting achievement towards the performance of the filter in terms of handling nonlinear problems and it achieves less computational burden by avoiding the manual selection of adjustable parameters. We call this new algorithm the AMFLMS algorithm. The predictive performance for the nonlinear chaotic Mackey Glass and Lorenz time series was observed and evaluated using the classical LMS, Kernel LMS, MFLMS, and the AMFLMS filters. The simulation results for the Mackey glass time series, both without and with noise, confirm an improvement in terms of mean square error for the proposed algorithm. Its performance is also validated through the prediction of complex Lorenz series.     

交通流量序列混沌特性分析及DFPSOVF预测模型

以实际采集的交通流量序列作为研究对象, 分别应用互信息法和虚假邻点法确定其延迟时间和最佳嵌入维数, 完成交通流量序列的相空间重构. 通过计算交通流量序列的饱和关联维数和最大Lyapunov指数判定其混沌特性. 以最小均方(LMS)算法为基础, 构建了一种基于Davidon-Fletcher-Powell方法的二阶Volterra模型(DFPSOVF), 其应用了一种可随输入信号变化而实时变化的基于后验误差假设的可变收敛因子技术. DFPSOVF模型避免了在Volterra模型中采用LMS自适应算法调整系数时参数选择不当引起的问题. 将DFPSOVF模型应用于具有混沌特性的短时交通流量预测, 结果表明: 当模型记忆长度与交通流量序列的嵌入维数选择一致时, 模型的预测精度较高, 可以满足交通诱导和交通控制的需要, 为智能交通控制提供了新方法、新思路及工程应用参考. 关键词： 交通流量 混沌 DFPSOVF模型 预测     

White noise nonlinear system analysis in nuclear magnetic resonance spectroscopy

Nonlinear noise excitation in nuclear magnetic resonance is a form of nonlinear spectroscopy which exploits the nonlinear susceptibilities in a very direct way. The nonlinear susceptibilities are defined by perturbation theory in the frequency domain. In nonlinear system analysis, on the other hand, the system response is described by a Volterra series in the time domain. The kernels of the Volterra functionals carry the information about the system and are to be determined by experiment.The series expansion of a molecular, atomic or nuclear system response is derived in quantum mechanics by time dependent perturbation theory, leading to a Volterra series with time ordered, triangular kernels. The kernels are multi-dimensional products of decaying exponentials, which describe coherence decays of particular density matrix elements. The Fourier transforms of the triangular Volterra kernels are the susceptibilies, which are formally identical in NMR spectroscopy and nonlinear optical spectroscopy. The nonlinear susceptibilities are multi-dimensional spectra, which in NMR spectroscopy reveal the spin communication pathways. These are established by various forms of single quantum coherence connectivities, such as indirect coupling, chemical exchange, cross-relaxation, dipolar and quadrupolar coupling.If the functionals of the Volterra series are orthogonalized with respect to Gaussian white noise excitation, the Wiener series results. The Wiener kernels can be derived by multi-dimensional cross-correlation of the system response with different powers of the Gaussian white noise excitation.Cross-correlation of the transverse magnetization response to noise excitation in NMR leads to multi-dimensional time functions, the Fourier transforms of which closely resemble the nonlinear susceptibilities. The cross-correlation spectra differ from the susceptibilities in the governing Liouvillean and the dynamic density matrix, which are affected by saturation for continuous excitation. Cross-correlation spectra and susceptibilities converge for vanishing excitation power. Therefore the cross-correlation spectra are referred to as stochastic susceptibilities.In stochastic NMR spectroscopy only odd order susceptibilities exist for transverse magnetization. The first nonlinear order is the third, and the nonlinear spectral information is derived from the third order susceptibility. Higher order susceptibilities are not feasible to derive from experimental data. An important share of the nonlinear information is found on the six subdiagonal 2D cross-sections through the third order susceptibility. These cross-sections arise in three pairs, which carry distinct information, separated according to longitudinal magnetization and population effects, zero quantum coherences, and double quantum coherences.In practice a nonlinear 3D spectrum is computed from experimental data by an algorithm in the frequency domain, which yields access to selected regions in the 3D spectrum. This spectrum is the symmetrized stochastic third order susceptibility. All its sub-diagonal 2D cross-sections are equivalent. They are the average of the six different sub-diagonal 2D cross-sections through the asymmetric third order susceptibility.The stochastic excitation technique in NMR is characterized by several unique attributes. (1) There is no minimum time for a data acquisition cycle, so that, at the expense of signal-to-noise ratio, strong samples can be investigated faster with stochastic NMR than with pulsed FT NMR. (2) Stochastic excitation tests the sample extensively, and measures a maximum amount of information in a single experiment. This feature is of particular interest for investigation of short-lived samples and of samples with little a priori information. (3) An experiment with stochastic excitation is simple to perform, but the data processing is more complex than in FT spectroscopy. (4) The nonlinear information about spin communication pathways is derived for individual frequency regions only, which are identified in the stochastic ID spectrum. This information is located primarily on the sub-diagonal 2D cross-sections through the third order susceptibility. (5) Stochastic NMR spectra derived from random noise excitation are contaminated by systematic noise. In the sub-diagonal 2D cross-sections the noise is reduced by filtering and symmetrization during data processing. (6) Sub-diagonal 2D cross-sections are sensitive to experimental phase distortions in one direction only. They are readily adjusted in phase with the same parameters as the ID spectrum. (7) Stochastic multi-dimensional spectra can be computed at variable resolution from one and the same set of raw data.So far stochastic NMR spectroscopy is not applied routinely in analytical spectroscopy. More practical experience is needed to evaluate its merits in comparison with Fourier transform NMR.Stochastic excitation is distinguished from continuous wave and sparsely pulsed excitation by low input power in connection with large bandwidth. This important property cannot be exploited in high resolution NMR in liquids, because excitation power is not a restricting factor in this case. The situation is different in NMR imaging, where large field gradients require large bandwidths and the excitation power becomes a point of concern. For this reason stochastic RF excitation is being investigated in NMR imaging.The multi-dimensional cross-correlation functions obtained from random noise excitation generally are contaminated by systematic noise. The occurrence of systematic noise can be avoided if pseudo-random excitation is used in combination with a transformation of the system response to obtain the kernels. This technique is used successfully in Hadamard spectroscopy, where the linear Volterra kernel is the Hadamard transform of the linear response functional. Nonlinear transformations^(220,221) for retrieval of nonlinear kernels have not yet been realized in NMR spectroscopy.The cross-correlation technique underlying the data evaluation in stochastic nonlinear system analysis is equivalent to interferometry in optical spectroscopy. The Michelson interferometer is the most prominent optical correlator. The time resolution of the kernels derived by cross-correlation is determined by the inverse bandwidth of the excitation. With the Michelson interferometer a time resolution of 10⁻¹⁴ s is achieved in IR spectroscopy. Since the IR correlogramm is Fourier transformed for spectral analysis, the time resolution cannot be exploited otherwise. For analysis of fast time dependent processes a two-dimensional interferometer should be constructed, which performs a 2D cross-correlation of the system response to two in general different noise inputs. One input pumps the time dependent process, the other is used to investigate the time dependence spectroscopically. This technique is introduced by the name of ‘two-dimensional interferometry’. It uses low excitation power, but provides high time resolution at large response energy. Related work is pursued in nonlinear optical spectroscopy with incoherent excitation. In this area the use of broad band lasers is investigated for generation of echoes and for correlation based measurements of relaxation times.     

改进阈值提升小波和自适应滤波器的开放光路红外光谱去噪

大气污染物的主要组成成分为挥发性有机物(VOCs),傅里叶变换红外光谱技术(FTIR)是现阶段应用广泛的挥发性有机物在线测量方法。开放光路获取到的大气红外光谱(OP-FTIR)易受各种噪声污染,如何有效、快速的去除红外光谱中的噪声是大气在线实时监测系统研究的热点。综合利用提升小波变换结构简单、运算量低的优点以及最小均方误差自适应滤波器的自动调节参数以达最优化滤波的性能,提出了一种改进阈值提升小波结合自适应滤波的红外光谱去噪算法。该算法先通过改进阈值小波系数的提升小波去噪,在去噪的同时保留更多光谱特征信息,然后使用提升小波变换分解出的高频系数重构出噪声相关信号,将其作为最小均方误差自适应滤波器的参考输入进行二次滤波处理,最终获得的去噪信号很好的去除了与特征光谱频谱重叠的噪声信号。分别对人工添加噪声的标准红外光谱和合肥市市区上空实测开放光路红外光谱进行去噪处理,结果显示使用该算法处理后的光谱信噪比(SNR)较离散小波传统阈值去噪方法高出3db,均方根误差(RSME)平均减少30%左右,运行时间减少46%。表明该算法计算简单、运行速度快,对于大气环境监测实时消噪系统具有重要的实际应用意义。     

混沌时间序列的自适应高阶非线性滤波预测

根据混沌序列产生的确定性和非线性机制,基于Volterra级数展式和混沌序列高阶奇异谱特征,提出了一种高阶非线性傅里叶红外(HONFIR)滤波预测模型用于混沌时间序列的自适应预测.其自适应算法采用时域正交算法来自适应地跟踪混沌的运动轨迹,而不是重构混沌系统 的全局或局部运动轨迹.实验研究表明：(1)这种HONFIR自适应滤波器能够有效地预测一些超 混沌序列.(2)预测混沌序列的性能与预测模型的非线性拟合能力有关,但并非非线性程度越 高,预测性能就越好.(3)当HONFIR滤波器对混沌序列的非线性拟合精度高时,其自适应预测 的性能与其输入维数的关系不受Takens嵌入定理的约束.(4)HONFIR自适应滤波器具有一定的 抗噪能力. 关键词： 混沌 非线性模型 滤波器     

投影寻踪网络用于光纤陀螺噪声消除的研究

提出采用投影寻踪网络逼近Volterra滤波器的方法以实现光纤陀螺输出信号非线性噪声消除.投影寻踪网络采用批量学习和参数交替优化的训练算法,可以自适应确定神经网络的规模、参数和神经元函数,具有简捷的网络结构和较强的鲁棒性,克服了Volterra滤波器随着阶数增加,滤波系数的数量呈几何级数增长,实现困难的问题.仿真结果表明PPLN网络滤波器比Volterra滤波器具有更好的消噪效果.     

Application of the Volterra filter in experimental diffraction data processing

The effective signal in x-ray diffraction analysis of material properties often contains high frequency (noise) and low frequency (trend) components as additive parts. It is necessary to extract the effective signal from the noise to ensure high quality of signal processing. Digital filters of Volterra type are proposed for filtering purposes and a comparison of Volterra filtration implemented on x-ray diffraction data versus results from a set of other digital filters is given.     

用一种少参数非线性自适应滤波器自适应预测低维混沌时间序列

基于混沌动力系统的相空间延迟坐标重构,利用混沌序列固有的确定性和非线性,提出了用 于混沌时间序列预测的一种少参数非线性自适应滤波预测模型.该预测模型在Volterra自适 应滤波器的基础上引入sigmoid函数来减少待定参数.实验研究表明,这种少参数非线性自适 应滤波预测器仅需用50个样本经20次预训练后,就能有效地预测一些低维混沌序列,且这种 少参数非线性自适应滤波预测器更便于工程实现. 关键词： 混沌 非线性自适应预测 少参数非线性自适应滤波器 自适应算法     

Nonlinear interactions in a rotating disk flow: From a Volterra model to the Ginzburg-Landau equation

The physical system under consideration is the flow above a rotating disk and its cross-flow instability, which is a typical route to turbulence in three-dimensional boundary layers. Our aim is to study the nonlinear properties of the wavefield through a Volterra series equation. The kernels of the Volterra expansion, which contain relevant physical information about the system, are estimated by fitting two-point measurements via a nonlinear parametric model. We then consider describing the wavefield with the complex Ginzburg-Landau equation, and derive analytical relations which express the coefficients of the Ginzburg-Landau equation in terms of the kernels of the Volterra expansion. These relations must hold for a large class of weakly nonlinear systems, in fluid as well as in plasma physics. (c) 2000 American Institute of Physics.     

基于Volterra滤波器的参量阵扬声器互调失真补偿研究

针对单边带调制方法预处理的参量阵扬声器系统在复杂信号输入情况下存在明显互调失真的问题,引入Volterra滤波器模型和简化的一维Volterra滤波器模拟了其互调失真。设计了相应的逆滤波器,并对其抑制系统互调失真的性能进行了实验对比。结果表明,所设计的逆滤波器都能有效降低系统的互调失真,而后者具有较低的计算复杂度。     

Modal control of beam flexural vibration

An active control system was developed to control the flexural vibrations of a beam with a modal filtering with only one secondary actuator. Segmented piezoelectric actuators and sensors were used for driving and sensing the bending beam vibrations. The primary actuator was fed by a broadband random disturbance signal in order to excite the first five modes of the structure. However, only the second to fifth modes were controlled. The control algorithm was implemented on a DSP board and the input and output signals were filtered using high order low pass filters. These filters, implemented on the DSP board avoid the degrading effect on the control performances of the higher order modes and which are not controlled. The modal filtering was achieved by computing. To this end, it is based on a previous identification procedure. This latter models, in one step, the dynamics of the structure and also the transfer function of the electronic circuits of the controller. The identified filtered modes were then used to compute the gain matrix using a LQR technique (linear quadratic regulator). Simulations of the active control were carried out and practical implementation of the control algorithms was performed. Experimental and simulation results were then compared and discussed.     

基于分数阶最大相关熵算法的混沌时间序列预测

为提高最大相关熵算法对混沌时间序列的预测速度和精度,提出了一种新的分数阶最大相关熵算法.在采用最大相关熵准则的基础上,利用分数阶微分设计了一种新的权重更新方法.在alpha噪声环境下,采用新的分数阶最大相关熵算法对Mackey-Glass和Lorenz两类具有代表性的混沌时间序列进行预测,并分析了分数阶的阶数对混沌时间序列预测性能的影响.仿真结果表明:与最小均方算法、最大相关熵算法以及分数阶最小均方算法三类自适应滤波算法相比,所提分数阶最大相关熵算法在混沌时间序列预测中能够有效地抑制非高斯脉冲噪声干扰的影响,具有较快收的敛速度和较低的稳态误差.     

Intermodulation distortion of the single-mode laser-diode

A detailed study of laser-diode intermodulation distortion (IMD) using the Volterra model that was developed for the single-mode laser-diode with optoelectronic feedback is presented in this paper. The IMD is analyzed by analyzing the amplitudes of the output sinusoids of various frequencies resulting from an input which is the sum of two sinusoids. The sinusoids at all frequencies due to the first-, second-, and third-order Volterra operators were determined and are discussed in this paper. The ones with frequencies which are not used in practical applications are included since, even though they are not in the pass-band of the first-order operator, they still can possibly effect system performance.