基于混沌理论的汉语语音信号的分析与预测

目前语音信号的分析与预测都是采用线性理论和线性预测技术,而语音信号的产生系统是一个复杂的非线性时变系统,而且具有混沌性,所以采用线性方法是不够的.深入研究了汉语语音信号的非线性特性,包括相空间重构理论及延迟时间、嵌入维数等相空间重构参数的确定方法,并求解出汉语语音音素的李雅普诺夫指数、延迟时间、嵌入维数和关联维数,所得结果表明汉语语音信号既非确定性的信号,又非随机信号,而是具有混沌特性的信号;根据汉语语音音素的延迟时间及嵌入维数的均值确定RBF神经网络(Radical Basis Function Network)模型中三层网络的神经元个数,结合 RBF 神经网络分析方法构造了一个非线性预测模型.仿真结果表明:基于 RBF 神经网络构造的非线性预测模型与线性预测模型相比,预测误差明显减小,预测性能上有所提高.     

应用混沌理论对煤矿瓦斯浓度预测

利用混沌时间序列短期可以预测的特点,构建煤矿瓦斯浓度预测模型,重构煤矿瓦斯浓度相空间,采用改进的C-C算法确定相空间时延t和相空间嵌入维数m。然后在重构相空间中,运用加权一阶局域法构建煤矿瓦斯浓度的预测模型,进行煤矿瓦斯浓度预测。     

基于快速极限学习机和差分进化的机场噪声预测模型

该文针对传统机场噪声预测模型存在的建模成本高、实用性差的不足,引入时间序列相空间重构理论,提出一种新的基于快速极限学习机和差分进化算法的机场噪声一体化预测模型。该模型利用相空间重构理论对机场噪声时间序列进行重构,并使用快速极限学习机对重构的相空间矢量进行学习建模,同时采用改进的差分进化算法实现对重构参数和模型参数的同步优化选择,整个建模过程简洁高效,无需人工干预。实验结果表明,该一体化预测模型能较好地跟踪机场噪声的变化趋势,且具有较同类模型更小的预测误差。     

基于PSO-Elman模型的网络流量预测

针对网络流量的混沌性特点以及传统神经网络处理网络流量预测问题易陷入局部极小导致预测精度不高的问题,提出在相空间重构基础上,采用粒子群算法(PSO)优化Elman神经网络初始参数的网络流量预测模型。首先对网络流量时间序列进行相空间重构,将重构后的流量序列作为模型的输入;再利用PSO算法全局搜索能力对Elman神经网络初始参数进行优化;最后利用训练好的Elman神经网络对网络流量进行预测。仿真结果表明,相比其他流量预测方法,基于PSOElman模型的网络流量预测提高了预测准确率。     

基于相空间重构实现非线性语音清浊音判决

以相空间重构理论为基础,采用Takens定理重构语音信号相空间并提取相似序列重复度(RPT)特征参数。利用清浊音RPT参数的差异,提出并实现了一种采用BP神经网络进行非线性清浊音判决的方法,得到了明显优于传统算法的结果。本文方法为语音特征提取和识别研究提供了新的途径     

遗传优化神经网络的网络流量预测

为了提高网络流量预测精度,利用相空间重构和预测模型参数间的相互联系,提出一种遗传算法优化神经网络的网络流量预测模型.首先将相空间重构和神经网络参数进行编码,网络流量预测精度作为目标函数,然后通过遗传算法选择模型最优参数,最后进行网络流量仿真实验.实验结果表明相对传统预测模型,遗传优化神经网络模型具有更高预测精度及稳定性更好.     

混沌信号的相空间重构和预测方法研究

基于复杂非线性系统的相空间重构理论和神经网络本质为非线性映射关系的特点,提出利用混沌时间序列重构相空间和BP神经网络构建其预测模型的方法。利用该方法对典型的Lorenz混沌时间序列进行了空间重构,研究了预测模型的预测效果,结果表明单步预测效果理想,多步预测在50步以内也能取得较小的预测误差,证明了混沌信号不同于随机噪声,具有短期可预测、长期不可预测的特征。该方法为具有混沌特性的时间序列如心电信号、电力负荷等预测模型的建立提供了理论基础。     

基于零极点预测模型的码激励线性预测语音编码算法

针对传统码激励线性预测（Code Excited Linear Predictive，CELP）语音编码器在预测模型和参数估计方面的不足，提出了一种基于零极点预测模型的CELP语音编码新算法。该算法采用零极点预测模型来更准确地描述语音信号的短时相关性，并采用梯度法来同时对零极点模型的参数和激励码本增益进行联合优化求解。实验结果表明所提语音编码算法可显著降低CELP编码器合成语音的归一化均方误差，有效提高合成语音的质量。     

基于相空间重构与最小二乘支持向量机的时延预测

针对网络控制系统的时延预测问题,提出一种基于相空间重构与最小二乘支持向量机的时延预测方法.首先利用0-1测试法确定时延序列具有混沌特性,引入相空间重构技术提高预测精度.对实际采集的时延序列进行Hurst指数分析,选择最小二乘支持向量机作为预测模型.然后利用C-C方法确定时延序列相空间重构参数,通过递归图确定时延序列的局部可预测性,利用遗传算法对最小二乘支持向量机的参数进行离线优化.最后通过优化后的最小二乘支持向量机并结合相空间重构对时延序列进行在线预测.与其它预测方法进行了仿真对比,结果表明本文方法具有更高的预测精度与更小的预测误差,同时并未降低预测算法的实时性.     

基于混沌时间序列和神经网络的网络流量预测方法

网络流量时间序列具有复杂的非线性和不确定性特征,故提出以相空间重构理论与递归神经网络相结合的网络流量预测方法。以相空间重构理论确定最佳延迟时间和最小嵌入雏数,重构网络流量时间序列。将重构后的时间序列运用递归神经网络来训练,得到合适的模型,并用于网络节点中网络流量的预测。将该方法应用于实际数据预测,其结果与传统的时间序列预测方法结果相比较,提高了预测精度和稳定性,证明了该预测模型和方法在实际时间序列预测领域的有效性和实用性。     

SELECTION OF PROPER EMBEDDING DIMENSION IN PHASE SPACE RECONSTRUCTION OF SPEECH SIGNALS

In phase space reconstruction of time series, the selection of embedding dimension is important. Based on the idea of checking the behavior of near neighbors in the reconstruction dimension, a new method to determine proper minimum embedding dimension is constructed. This method has a sound theoretical basis and can lead to good result. It can indicate the noise level in the data to be reconstructed, and estimate the reconstruction quality. It is applied to speech signal reconstruction and the generic embedding dimension of speech signals is deduced.     

混沌时间序列的Volterra自适应预测滤波器定阶

由于Volterra自适应滤波器的阶数对预测性能有较大的影响,在实际预测中,如何确定Volterra自适应滤波器的最优阶数就成为一个关键问题,该文运用相空间重构理论,推导出了Volterra自适应滤波器的最优阶数等于混沌动力系统的最小嵌入维数,作者用六种混沌时间序列进行实验,结果表明这种定阶方法在混沌时间序列Volterra自适应预测中非常成功,该方法对噪声影响的变化,表现出较好的鲁棒性。     

语音信号相空间重构中嵌人维数的选择

嵌入维是时间序列相空间重构中的基本参数。本文基于增大重构维以减少虚邻点的思想,构造了一种求合适最小嵌入维的方法。文章详细讨论了本方法的原理,给出了具体的算法构造,分析了算法性能,比较了本方法与已有同类方法的不同。用本方法,除可得到适合的最小嵌入维外,还可评估重构数据中所含的噪声强弱。并得到重构质量评价。将本方法应用于语音信号相空间重构,得到了语音信号一般情况下的嵌入维数。     

Efficient implementation of class of isotropic quadratic filters byusing Walsh-Hadamard transform

A method is proposed for the efficient implementation of a class of second-order Volterra filters where their quadratic kernels are recursively constructed from a set of isotropic subkernels (ISKs). The quadratic kernel of this class of filters can be factorised into a diagonal form by a Walsh-Hadamard transform (WHT) and be implemented using a transform and multiply structure. An algorithm for determining the quadratic ISK kernel for the nonlinear prediction of speech signals is presented     

PARAMETERS DETERMINATION METHOD OF PHASE-SPACE RECONSTRUCTION BASED ON DIFFERENTIAL ENTROPY RATIO AND RBF NEURAL NETWORK简

Phase space reconstruction is the first step of recognizing the chaotic time series. On the basis of differential entropy ratio method, the embedding dimension mopt and time delay τ are op- ritual for the state space reconstruction could be determined. But they are not the optimal parameters accepted for prediction. This study proposes an improved method based on the differential entropy ratio and Radial Basis Function （RBF） neural network to estimate the embedding dimension rn and the time delay τ, which have both optimal characteristics of the state space reconstruction and the prediction. Simulating experiments of Lorenz system and Doffing system show that the original phase space could be reconstructed from the time series effectively, and both the prediction accuracy and prediction length are improved greatly.     

Use of meixner functions in estimation of Volterra kernels of nonlinear systems with delay

Volterra series representation of nonlinear systems is a mathematical analysis tool that has been successfully applied in many areas of biological sciences, especially in the area of modeling of hemodynamic response. In this study, we explored the possibility of using discrete time Meixner basis functions (MBFs) in estimating Volterra kernels of nonlinear systems. The problem of estimation of Volterra kernels can be formulated as a multiple regression problem and solved using least squares estimation. By expanding system kernels with some suitable basis functions, it is possible to reduce the number of parameters to be estimated and obtain better kernel estimates. Thus far, Laguerre basis functions have been widely used in this framework. However, research in signal processing indicates that when the kernels have a slow initial onset or delay, Meixner functions, which can be made to have a slow start, are more suitable in terms of providing a more accurate approximation to the kernels. We, therefore, compared the performance of Meixner functions, in kernel estimation, to that of Laguerre functions in some test cases that we constructed and in a real experimental case where we studied photoreceptor responses of photoreceptor cells of adult fruitflies (Drosophila melanogaster). Our results indicate that when there is a slow initial onset or delay, MBF expansion provides better kernel estimates.     

Data-mining based fault detection

This paper presents a fault-detection method based on the phase space reconstruction and data mining approaches for the complex electronic system. The approach for the phase space reconstruction of chaotic time series is a combination algorithm of multiple autocorrelation and Г-test, by which the quasi-optimal embedding dimension and time delay can be obtained. The data mining algorithm, which calculates the radius of gyration of unit-mass point around the centre of mass in the phase space, can distinguish the fault parameter from the chaotic time series output by the tested system. The experimental results depict that this fault detection method can correctly detect the fault phenomena of electronic system.     

语音信号非线性分析与处理

介绍基于混沌理论的语音信号非线性分析和处理。概括语音信号非线性和混沌的依据;描述了语音信号混沌特性参量,包括Ｌｙａｐｕｎｏｖ指数和分形维;讨论运用Ｔａｋｅｎｓ嵌入定理于语音信号相空间重构时的研究议题,包括嵌入维数、时间延迟、等;最后阐述应用ＲＢＦ神经网络进行基于混沌的语音信号非线性处理。