混沌跳频通信的非线性自适应预测对抗

基于混沌跳频码序列构造固有的确定性、非线性等特点,提出了对抗混沌跳频通信的非线性自适应预测对抗的新概念.以Logistic-Kent映射设计的混沌跳频码为例,采用基于Sigmiod函数的Volterre自适应滤波预测器对其进行预测研究.仿真实验结果表明非线性自适应预测器比神经网络预测器具有更高的预测对抗效果.     

基于背景杂波自适应预测的红外弱小目标检测

文章根据背景杂波和运动目标的红外成像差异,提出了两种自适应杂波预测技术的低 信噪比小目标检测方法。首先讨论了一种基于均方误差最小的自适应线性预测器,该预测器计算量小,易满足实时要求,且对平稳和线性云层红外背景图像具有很好的背景预测能力。然后提出了一种基于非线性函数可调整的BP 神经网络预测器,该预测器中的非线性函数可调整且非线性程度很高,能很好的适应各种复杂的起伏背景,特别是非平稳和非线性杂波背景。文中还通过实际的红外图像验证了两种方法的有效性。     

基于后向回归网络的自适应多步预测

本文针对一类非线性动态系统,提出了一种新的基于后向回归网络的自适应多步预测方法,并对基于神经网络的自适应预测机理进行了分析。预测器由两个同构的后向回归网络来实现,输入及预测长度由随机读写存贮器单元的取值来控制。计算机仿真结果表明,这种自适应预测方法对一类恒定参数的非线性系统是可行的,可有效地处理系统具有的大时延和随机干扰。     

一种抑制直扩通信窄带干扰的新型非线性自适应预测滤波器

为了更有效地抑制直接序列扩频通信中的窄带干扰,论文提出了一种非线性Laguerre滤波抑制直扩频通信窄带干扰的方法,给出相应的自适应算法。该方法利用具有IIR和FIR滤波器特点的Laguerre时延单元构造非线性自适应预测滤波器。该方法可以使预测器的记忆深度与预测器的阶数解偶,以更少的阶数达到更优的抑制性能。仿真实验结果表明:与Vijayan和Poor传统非线性自适应预测滤波器相比,该滤波器能够保证均方误差的收敛稳定性,并能在信噪比改善提高12dB的前提下,使滤波器阶数降低为原来的1/3~1/5,具有一定的现实意义。     

对频率捷变雷达频率的预测

在分析几种伪随机码序列的混沌动力学特征基础上,以Logistic-Kent变频码序列的预测为例,着重研究了自适应块sigmoid-Volterra滤波预测的性能。与BP神经网络预测器相比,非线性自适应滤波预测方法可在短观察数据、少训练或无需训练,并可得较好的预测结果,可望成为一种付诸工程实现的对频率捷变频雷达预测引导瞄频干扰的有效方法。最后给出了非线性自适应预测对抗技术今后的研究建议。     

基于LMS的图像自适应预测编码

为了提高图像编码预测器的预测性能,提出了一种低复杂度,高效的自适应预测方法。采用LMS(Least MeanSquare)自适应滤波技术进行预测,并对预测值进行减邻域均值的改进,有效克服了图像的非零均值和非平稳性特征,满足LMS算法的要求,使预测性能得以提高。通过对不同图像的仿真结果表明,该方法的预测差值图像的熵比GAP算法和MED算法的差值图像的熵要小0.1 bit/piexl左右,均方误差(MSE)也要小于后两者的均方误差。     

自适应高阶非线性FIR滤波预测器的最优输入维数

基于相空间重构理论,用条件概率确定了用于混沌预测的一种自适应高阶非线性预测滤波器的最优输入维数Nopt.理论分析和实验研究表明这种自适应高阶非线性预测滤波器的最优输入维数小于或等于混沌时间序列的最小嵌入维数,且与混沌时间序列的关联维数D2的关系为No.大于D2的最小正整数.     

有缺损数据的混沌时间序列预测

根据一种基于相空间邻点的非线性自适应滤波算法，研究了缺损数据怎样影响预测。仿真显示，缺损数据对预测有不同程度的影响，文中提出的解决方法是有效的。     

高频段频谱占用的Volterra预测方法研究

为了能够给高频用户提供较为可靠的空闲频段,需要对高频段频谱占用的情况进行预测。本文提出一种基于自适应Volterra滤波理论的预测方法,通过对频谱占用因子时间序列进行状态空间重构,并采用递推形式的自适应递归最小二乘算法实时调整Volterra模型的核系数。实际数据处理结果表明,该预测方法能够很好地跟踪频谱占用因子的非线性变化,具有预测误差小,训练计算复杂度低等优点。      

基于支持向量机的混沌跳频码预测

基于跳频码固有的混沌特性,结合统计学习理论的优点,探讨了支持向量机预测跳频码的方法,以Logistic-Kent及Logistic映射跳频码为例,使用支持向量机对其进行了预测研究并对嵌入维数的选取作了详细分析,为实际参数的选择提供了一定的依据.实验结果表明该方法具有很强的实用性,而且整体预测效果明显好于非线性自适应预测器及神经网络预测器.     

Prediction of chaotic time series based on the recurrent predictor neural network

Chaos limits predictability so that the long-term prediction of chaotic time series is very difficult. The main purpose of this paper is to study a new methodology to model and predict chaotic time series based on a new recurrent predictor neural network (RPNN). This method realizes long-term prediction by making accurate multistep predictions. This RPNN consists of nonlinearly operated nodes whose outputs are only connected with the inputs of themselves and the latter nodes. The connections may contain multiple branches with time delays. An extended algorithm of self-adaptive back-propagation through time (BPTT) learning algorithm is used to train the RPNN. In simulation, two performance measures [root-mean-square error (RMSE) and prediction accuracy (PA)] show that the proposed method is more effective and accurate for multistep prediction. It can identify the systems characteristics quite well and provide a new way to make long-term prediction of the chaotic time series.     

混沌光学系统的神经网络自适应控制研究

提出一种用于混沌光学系统控制的神经网络自适应控制技术。以一前向神经网络作为受控混沌光学系统的系统辩识器，由此神经网络系统辩识器与受控混沌光学系统输出差值作为负反馈对受控混沌光学系统控制参数进行调整达到控制目的。由于所使用神经网络系统辩识器在常规ＢＰ算法的支持下可从受控混沌光学系统的输出时间序列进行动力学模型重构，因而特别适用于对未知动力学表述的混沌光学系统进行控制。以对布喇格声光双稳混沌系统的系统辩识及自适应控制为例，对此神经网络自适应控制技术可行性进行了示例证明。     

Nonlinear adaptive prediction of speech with a pipelined recurrentneural network

New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined recurrent neural network (PRNN) are presented. A computationally efficient gradient descent (GD) learning algorithm, together with a novel extended recursive least squares (ERLS) learning algorithm, are proposed. Simulation studies based on three speech signals that have been made public and are available on the World Wide Web (WWW) are used to test the nonlinear predictor. The gradient descent algorithm is shown to yield poor performance in terms of prediction error gain, whereas consistently improved results are achieved with the ERLS algorithm. The merit of the nonlinear predictor structure is confirmed by yielding approximately 2 dB higher prediction gain than a linear structure predictor that employs the conventional recursive least squares (RLS) algorithm     

An adaptive bit time estimator

A bit time estimator which uses adaptive filtering techniques is presented. The filter weights of an adaptive linear predictor are shown to provide a reliable estimate of the bit time T of a random binary square wave contaminated with additive white Gaussian noise, with little or no a priori information. The quality of this estimator is then evaluated via the least mean square algorithm, and a comparison is made between it and a more conventional estimator based on a zero crossing detector. This comparison shows that an adaptive estimator based on a linear predictor is generally superior     

A fully adaptive normalized nonlinear gradient descent algorithm for complex-valued nonlinear adaptive filters

A fully adaptive normalized nonlinear complex-valued gradient descent (FANNCGD) learning algorithm for training nonlinear (neural) adaptive finite impulse response (FIR) filters is derived. First, a normalized nonlinear complex-valued gradient descent (NNCGD) algorithm is introduced. For rigour, the remainder of the Taylor series expansion of the instantaneous output error in the derivation of NNCGD is made adaptive at every discrete time instant using a gradient-based approach. This results in the fully adaptive normalized nonlinear complex-valued gradient descent learning algorithm that is suitable for nonlinear complex adaptive filtering with a general holomorphic activation function and is robust to the initial conditions. Convergence analysis of the proposed algorithm is provided both analytically and experimentally. Experimental results on the prediction of colored and nonlinear inputs show the FANNCGD outperforming other algorithms of this kind.     

Denoising Nonlinear Time Series by Adaptive Filtering and Wavelet Shrinkage: A Comparison

Time series measured in real world is often nonlinear, even chaotic. To effectively extract desired information from measured time series, it is important to preprocess data to reduce noise. In this Letter, we propose an adaptive denoising algorithm. Using chaotic Lorenz data and calculating root-mean-square-error, Lyapunov exponent, and correlation dimension, we show that our adaptive algorithm more effectively reduces noise in the chaotic Lorenz system than wavelet denoising with three different thresholding choices. We further analyze an electroencephalogram (EEG) signal in sleep apnea and show that the adaptive algorithm again more effectively reduces the Electrocardiogram (ECG) and other types of noise contaminated in EEG than wavelet approaches.      

自动巡航汽车自适应逆控制的建模与仿真

研究神经网络非线性系统的自适应建模和逆建模策略用于非线性的自动巡航系统的控制及可行性。通过对自适应逆控制方法与现行的反馈控制、模糊控制、PID控制进行对比,并在有干扰的情况下系统需要一定的收敛时间,通过运用Matlab软件进行仿真。根据仿真结果分析,当对象输出没有受到干扰时,其在线辨识对象模型和逆模型有十分好的效果;当对象输出存在一些干扰时,由于干扰的存在,需要一段时间来将两个辨识模型收敛。因此,基于动态神经网络的非线性自适应逆控制系统是十分可行的。     

混沌通信系统中自适应解调技术的仿真研究

在混沌参数调制技术中,为了有效地提取混沌系统参数,本文在文献[4]的基础上,对自适应滤波算法的性能进行了仿真研究,并修正了其中的错误.仿真结果表明自适应滤波技术能够快速收敛,并能克服其它混沌控制技术中只对慢变信号有效这一局限性.     

一种基于神经网络的超稳定自适应控制算法

提出了一种用于控制复杂非线性系统的超稳定自适应控制算法。使用波波夫超稳定性原理设计控制器。用神经网络在线辨识系统的建模误差及不确定性因素，辨识结果作为补偿信号以实现系统的鲁棒控制。对一双输入双输出非线性系统的仿真结果表明，所提出的超稳定自适应控制算法具有较好的性能。     

一种快速自适应的Harris角点检测方法研究

Harris角点检测算法在图像处理中使用非常广泛,有着较高检测率,但算法运算量比较大,实时性不高.同时,该算法无法设置通用阈值处理不同图像.针对这些问题,提出一种快速自适应Harris角点检测算法.该算法先使用Fast算法,对图像进行预筛选,再使用Harris算法,并构造自适应阈值.实验结果表明,该算法可以有效克服阈值选择不当造成的角点冗余或丢失,并可大幅减少运算量.