通信信道信号畸变的一种参数辩识方法

本文提出了一种信道在传输信号时使信号发生畸变的参数辩识方法,即ＭＡ模型方法。对于信道的畸变特征只需少量的几个参数就能描述,针对一般信号的非高斯分布特点,提出了用三阶累积量来估计模型参数的方法,即使在高斯色噪声的情况下,它也能有效地估计模型参数。计算简单,有效。     

基于图像分解的多核非线性扩散去噪方法

研究了一种基于图像分解的多核非线性扩散去噪方法,利用两个非线性扩散模型分别提取图像的主信号和细节信息。先建立一个基于边缘定向的非线性扩散模型,实现对图像的主信号的提取。然后利用P—M扩散方程提取残余图像中的高频信号。将两步处理得到的信号进行合成,得到最后的处理结果。该方法能充分利用各个不同模型的优势,在整幅图像上均具有较好的处理效果。仿真计算结果表明,经该方法处理后的图像与现有的非线性扩散去噪方法相比,其噪声抑制更充分、边缘更清晰、峰值信噪比更高。     

Hammerstein模型的记忆效应盲辨识方法

针对未知记忆深度的Hammerstein模型,提出一种基于高阶累积量的Hammerstein模型记忆效应盲辨识方法。将Hammerstein模型中对记忆深度的确定转换为对模型输出信号高阶累积量扩展矩阵的求秩问题,给出对角元素乘积(NPODE)方法以确定记忆深度,分别比较该方法与GM直接定阶法、拐点法的鲁棒性。结合提出的记忆深度估计算法,给出线性记忆模块系数的提取方法。理论推导与仿真结果表明,线性记忆模块系数的提取过程不受无记忆非线性效应的影响。     

用高阶累积量提取高斯噪声中信号的研究

在噪声中提取信号一直是微弱信号检测及数字信号处理领域里研究的重要问题，本文介绍的最如何利用高阶累积量提取高斯噪声中的信号，可以证明了，对于高斯噪声，它的三阶或三阶以上的累积量为零，因此，通过计算高斯噪声的高阶累积量可达到抑制噪声的目的。     

基于M估计的非线性鲁棒检测卡尔曼滤波算法

针对传统鲁棒非线性滤波在观测噪声为非高斯强干扰噪声情况下,滤波性能下降的问题,提出一种利用卡方检测法预判断的非线性鲁棒检测滤波算法。该算法通过卡方检测设置门限,剔除突变野值,利用M估计修正量测更新。仿真实验对比了几种典型非线性滤波方法在不同观测噪声环境下的性能。所提算法在非高斯强干扰噪声情况下,比传统鲁棒滤波算法估计精度平均提高了25.5%;估计方差平均减少了18.3%。实验结果表明:所提算法可以抑制观测量非高斯强干扰噪声的影响,提高滤波精度及稳定性。     

基于直接学习法的一种新预失真器

为了克服宽带信号经过记忆放大器的非线性失真,针对有记忆非线性功放的多项式模型,提出了一种新的基于直接学习法的自适应算法.该算法采用无记忆预失真器的级联扩展,具有横向滤波器结构,与记忆多项式有相似的线性化效果.并且针对信号噪声对自适应算法的扰动和收敛速度慢等缺点,采用归一化LMS算法加以改进.在非线性功放的记忆多项式模型下,通过宽带信号验证了基于直接学习法的记忆型预失真器算法的有效性.     

海洋噪声中舰船辐射噪声的一种检测方法

利用高阶谱分析的方法对实际的海洋环境噪声和舰船辐射噪声的高斯性和线性特性进行了检测,发现在一般情况下,短时间内的海洋环境噪声是高斯信号,而舰船辐射噪声信号则是非高斯和非线性的。它们的这些差异性可以用来进行舰船辐射噪声的检测,以及提高舰船噪声的各种处理的精度。     

基于高斯核函数的Hammerstein非线性系统辨识

提出了一种基于高斯核函数的Hammerstein非线性系统参数辨识方法。Hammerstein非线性系统由一个静态非线性模块和一个动态线性模块串联组成,利用高斯核函数神经网络和传递函数模型分别建立Hammerstein系统的静态非线性模块和动态线性模块。首先,基于可分离信号的输入输出数据,采用相关性分析方法估计动态线性模块的参数,有效抑制噪声的干扰。其次,针对Hammerstein非线性系统的不可测噪声项,利用残差的估计值代替不可测变量,推导了递推增广最小二乘辨识方法,根据随机信号的输入输出数据辨识静态非线性模块和噪声模型的参数。仿真结果表明,针对有色噪声干扰的Hammerstein非线性系统,所提方法具有较好的辨识精度和鲁棒性。     

基于伽玛通滤波器的双谱特征语音可懂度算法

针对现有的语音可懂度评价方法不能真实贴近人耳对语音的感知过程，提出一种基于人耳听觉特性的双谱特征预测语音可懂度评价(Gammatone-bspectral speech intelligibility metric, GBSIM)算法。充分利用双谱可以检测语音信号中的非线性相位耦合，抑制非高斯信号中的高斯噪声的特性，采用可以模拟人工耳蜗模型的Gammatone滤波器组，通过滤波处理将输入的语音信号分为32个听觉子频带，用三阶统计量对每个子频带的语音信号进行双谱估计并提取单一特征值来计算语音的可懂度。实例验证结果表明，该方法对信号失真变化敏感，其评价结果与主观评价具有很高的相关度，相对于传统的语音可懂度评价算法具有更好的评价效果。     

基于改进一维卷积和双向长短期记忆神经网络的故障诊断方法

针对工业领域中故障诊断数据存在时序性和夹杂强噪声的特点导致的收敛速度慢以及诊断精度低的问题,提出了一种基于改进一维卷积和双向长短期记忆（1DCNN-BiLSTM）神经网络融合的故障诊断方法。该方法包括故障振动信号的预处理、特征的自动提取以及振动信号的分类。首先,采用自适应白噪声的完整经验模态分解（CEEMDAN）技术对原始振动信号进行预处理;其次,构建1DCNN-BiLSTM双通道模型,将处理后信号输入双向长短期记忆（BiLSTM）神经网络模型和一维卷积神经网络（1DCNN）模型两个通道,从而对信号的时序相关性特征、局部空间的非相关性特征和弱周期性规律进行充分提取;然后,针对信号夹杂强噪声的问题,对压缩与激励网络（SENet）模块进行改进并将其作用于两个不同的通道;最后,输入全连接层将双通道提取的特征进行融合并借助Softmax分类器实现对设备故障的精确识别。使用凯斯西储大学轴承数据集进行实验,结果表明改进后的SENet模块同时作用于1DCNN通道和stacked BiLSTM通道,1DCNN-BiLSTM双通道模型在保证快速收敛的情况下有最高诊断精度96.87%,优于传统单通道模型,有效提高了机械设备故障诊断效率。     

基于遗传规划的哈默斯坦模型的辨识

该文基于遗传规划提出了一种辨识哈默斯坦模型的新方法。哈默斯坦模型由静态非线性模块和动态线性模块串联而成,因此系统辨识的目标是要找到非线性和线性模块的最优数学模型。该文通过遗传规划确定非线性模块的函数结构,并结合遗传算法确定模型的未知参数,适应度值的计算采用了最小信息量准则(A IC),以平衡模型的复杂度和精确度。该方法不需要对模型的先验知识有详细了解,就能达到较好的辨识效果,并且能够克服观测噪声的污染,获得参数的无偏估计。仿真结果说明了该方法的有效性。     

有色噪声干扰下Hammerstein非线性系统两阶段辨识

针对实际工业过程中普遍存在有色噪声,提出了有色噪声干扰下Hammerstein非线性系统两阶段辨识方法。采用设计的组合式信号实现Hammerstein系统各模块参数辨识分离,简化了辨识过程。在第一阶段,基于可分离信号的输入输出数据,利用相关分析算法估计线性模块参数,减少了有色噪声对辨识的干扰。在第二阶段,基于随机信号的...     

Auxiliary model-based least-squares identification methods for Hammerstein output-error systems

The difficulty in identification of a Hammerstein (a linear dynamical block following a memoryless nonlinear block) nonlinear output-error model is that the information vector in the identification model contains unknown variables—the noise-free (true) outputs of the system. In this paper, an auxiliary model-based least-squares identification algorithm is developed. The basic idea is to replace the unknown variables by the output of an auxiliary model. Convergence analysis of the algorithm indicates that the parameter estimation error consistently converges to zero under a generalized persistent excitation condition. The simulation results show the effectiveness of the proposed algorithms.     

非线性动态传感器系统的H模型神经网络辨识

针对非线性动态传感器模型辨识问题,提出一种新的Hammerstein模型神经网络结构辨识法。非线性动态传感器系统采用Hammerstein模型描述,将系统分解为非线性静态增益串接线性动态环节。再设计一种网络权系数对应于相应的Hammerstein模型参数的新型神经网络结构,推导了基于反向传播的网络权系数调整方法。通过网络迭代训练同时得到静态与动态两个环节的模型参数。最后通过一个H模型的数值仿真来验证方法的有效性,仿真结果表明所提辨识方法是有效的。     

Nonlinear system identification based on ANFIS-Hammerstein model using Gravitational search algorithm

The identification of real-world plants and processes, which are nonlinear in nature, represents a challenging problem. Currently, the Hammerstein model is one of the most popular nonlinear models. A Hammerstein model involves the combination of a nonlinear element and a linear dynamic system. On the other hand, the Adaptive-network-based fuzzy inference system (ANFIS) represents a powerful adaptive nonlinear network whose architecture can be divided into a nonlinear block and a linear system. In this paper, a nonlinear system identification method based on the Hammerstein model is introduced. In the proposed scheme, the system is modeled through the adaptation of an ANFIS scheme, taking advantage of the similarity between it and the Hammerstein model. To identify the parameters of the modeled system, the proposed approach uses a recent nature-inspired method called the Gravitational Search Algorithm (GSA). Compared to most existing optimization algorithms, GSA delivers a better performance in complex multimodal problems, avoiding critical flaws such as a premature convergence to sub-optimal solutions. To show the effectiveness of the proposed scheme, its modeling accuracy has been compared with other popular evolutionary computing algorithms through numerical simulations on different complex models.     

Nonlinear model predictive control of multivariable processes using block-structured models

Block-structured models, such as Wiener or Hammerstein models, have been used in nonlinear model predictive control to reduce the cost of identification and online computation. The solution of a nonlinear dynamic optimization problem has been avoided by inverting the nonlinear element and solving the resulting linear problem in the past. However, by exploiting the block structure for sensitivity calculation, the original nonlinear problem can also be solved at low computational cost. At the same time, greater modeling flexibility is achieved. Recently, a new Hammerstein model structure has been proposed for multivariable processes with input directionality, which exploits such increased modeling flexibility. This paper deals with nonlinear model predictive control constrained by models of Hammerstein or Uryson structure. A method for efficient calculation of sensitivity information is developed. In a simulation example, the method is shown to combine low computational cost with a significant reduction of the loss of optimality compared to the previous methods.     

Hammerstein system identification through best linear approximation inversion and regularisation

Hammerstein systems are composed by the cascading of a static nonlinearity and a linear system. In this paper, a methodology for identifying such systems using a combination of least squares support vector machines (LS-SVM) and best linear approximation (BLA) techniques is proposed. To do this, a novel method for estimating the intermediate variable is presented allowing a clear separation of the identification steps. First, an approximation to the linear block is obtained through the BLA of the system. Then, an approximation to the intermediate variable is obtained using the inversion of the estimated linear block and the known output. Afterwards, a nonlinear model is calculated through LS-SVM using the estimated intermediate variable and the known input. To do this, the regularisation capabilities of LS-SVM play a crucial role. Finally, a parametric re-estimation of the linear block is made. The method was tested in three examples, two of them with hard nonlinearities, and was compared with four other methods showing very good performance in all cases. The obtained results demonstrate that also in the presence of noise, the method can effectively identify Hammerstein systems. The relevance of these findings lies in the fact that it is shown how the regularisation allows to bypass the usual problems associated with the noise backpropagation when the inversion of the estimated linear block is used to compute the intermediate variable.     

A new approach for nonlinear process identification using orthonormal bases and ordinal splines

A new gray-box method for nonlinear process identification is presented. Industrial deployment for model predictive control (MPC) is the primary focus of this development. For flexibility, the identification accommodates Hammerstein, Wiener and the more general N–L–N block-oriented structures. Instruments comprised of linear and nonlinear combinations of inputs and outputs are also accommodated. Unique to this approach is the utilization of two sets of bases. One is constructed using an estimate of the process poles and the other is constructed using a predefined set of special cubic splines. An intriguing aspect of this formulation is that nonlinear dynamics are implicitly accommodated. In addition, problems associated with identifying the linear portion of the model in conventional block oriented formulations are removed. Because of the bases formulation, it is possible to solve the identification problem for many supported structures by convex optimization and hence avoid the inherent problems of iterative solutions. To insure open-loop unbiased estimates, any structures using output nonlinearities do require an iterative solution. Two test cases from the open literature are presented as are results from plant step-test data on a problematic air separation unit.     

Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.