基于递阶原理的非均匀采样非线性系统的模糊辨识

针对非均匀多采样率非线性系统的建模问题,提出了基于递阶原理的模糊辨识方法.首先,分析了非线性系统在输入信号非均匀周期刷新,输出信号周期采样的情况下,非线性系统可以通过提升技术,利用多个局部线性模型加权组合的模糊模型来描述.在此基础上,利用GK模糊聚类确定模糊模型前件结构,利用基于递阶原理的递推最小二乘辨识算法辨识模糊模型后件参数.同时,通过鞅定理对辨识算法的收敛性进行了研究.最后,通过仿真实例证明了本文方法的有效性.     

一种多输入多输出系统的盲辨识算法

多输入多输出线性系统的盲辨识问题可以利用输出信号的高阶累积量来解决.针对已有的一个线性MIMO系统辨识方法没有充分利用累积量矩阵固有结构的不足,提出一个改进算法,从而提高估计性能.并通过计算机仿真作了验证.     

基于子空间跟踪的状态空间系统辨识方法及其在时变系统辨识中的应用

基于系统状态空间模型的系统辨识方法的一个困难在于算法具有较大的运算量和存储量。本文将基于APEX算法的子空间跟踪方法引入辨识算法。APEX算法的神经网络实现可以有效地减少辨识算法的运算量和存储量。本文将得到的算法应用于时变系统的辨识，仿真结果和应用于实际数据的结果验证了方法的有效性。     

系统辨识研究的现状

综述了系统辨识问题的研究进展,介绍了经典的系统辨识方法及其缺点,引出了将集员、多层递阶、神经网络、遗传算法、模糊逻辑、小波网络等知识应用于系统辨识得到的一些现代系统辨识方法,最后总结了系统辨识今后的发展方向。     

一种基于FIR滤波器的非均匀小波周期采样方法

针对Shannon采样定理只能处理带限信号和要求采样率不低于Nyquist率的缺陷,研究了小波空间中的一种非均匀周期采样理论,给出了定理成立的条件及其突破Nyquist率限制的理论依据,将采样理论扩展到了非带限信号领域。对于紧支尺度函数张成的子波空间中的任意信号,可以利用非均匀周期采样所得的样本以及正交镜像滤波器理论求出其小波系数的估计值,进而得到信号的重建表达式。该方法在信号重建的过程中用到的全是有限冲击响应滤波器,避免了无限冲击响应滤波器的出现,降低了实际物理实现的难度。计算机仿真结果表明该方法是切实有效的,信号重建的相对误差小于1%。     

基于多率采样的永磁同步电机控制方法设计

在工业自动化领域,永磁同步电动机在中小功率控制系统中发挥着重要作用。本项目采用多采样率方法构建永磁同步电机控制系统,其主要解决的问题包含多采样率下的速度及参数辨识、永磁同步电机多采样率滑模变结构控制系统设计。     

光电跟踪架传递函数辨识方法及应用

鉴于传统的光电跟踪伺服控制系统频率响应测试方法复杂的缺点,采用了一种全数字化频率响应测量方案对光电跟踪架的频率特性进行测试。提出了一种递阶辨识法与传递函数参数辨识算法相结合进行传递函数辨识的新方法,并推导出了适用于不同阶次传递函数的辨识算法数学模型。在使用递阶辨识法辨识出系统阶次的基础上,利用测试得到的数据,分别采用最小二乘法和以新推导出的数学模型为基础的Levy法、Sanko法和Vinagre法辨识出了光电跟踪架的传递函数。辨识结果表明,新推导出来的数学模型正确,以其为基础的辨识算法误差均小于最小二乘法;3种算法中,Sanko法在整个频域内的辨识效果最好;采用递阶辨识原理与参数辨识算法相结合的方法可行,且精度较高。     

基于累计量辨识非最小相位FIR系统的新方法

该文考虑用带有噪声输出数据的累计量实现对非最小相位ＦＩＲ系统的参数辨识问题。提出一个新的基于高阶累计量的方法。其特点如下,（１）灵活性：采用了两个任意阶次相邻的输出累计量;（２）线性：方法的表达式相对于未各量为线性。这不同于其它一些已存在的算法,因而,避免了额外的滞后处理,可提高参数估计的准确性。本文在ＡＲＭＡ高斯噪声及三种实际噪声情况下,做了大量的实验。结果表明,本文提出的算法不仅能有效地完成参     

一种特别的多用户系统的非盲辨识方法

本文把循环频率域作为一个新的维度来对接收信号进行处理,提出了一种多用户线性时不变系统的非盲检测方法。     

一种基于模糊神经网络的非线性系统模型辨识方法

该文提出一种非线性系统的模型辨识方法。通过结构的辨识(学习)和参数的辨识(学习),构造了一个模糊神经网络,经调整网络的权值,获得一个精确的模糊模型。对两个非线性系统辨识的仿真结果验证了该方法的有效性。     

Nonlinear system identification

This paper provides an overview of nonlinear system identification methodologies. The theory and application of nonlinear system identification is vast, and this overview is not intended to be comprehensive. Rather, the attempt here is to illustrate some of the salient features and key aspects of nonlinear system identification, especially those most relevant to the practitioner. In particular, this overview focuses on important issues in nonlinear system idenfication that differ from those encountered in linear system identification, including tests for nonlinearity, model selection, and input signal considerations.     

应用粒子群优化的非线性系统辨识

提出了一种应用粒子群优化的非线性系统辨识方法。首先将非线性系统的辨识问题转化为参数空间上的优化问题,然后利用粒子群优化算法对整个参数空间进行高效并行搜索以获得系统参数的最优估计。以Hammerstein模型的辨识为例说明了本方法的可行性。     

具有维纳-哈默斯坦模型结构非线性 系统的一种辨识方法

 本文综合应用相关分析法、多维z变换、解卷积和最小二乘法提出了一种新的辨识具有维纳-哈默斯坦模型结构非线性系统的方法.采用这种方法,维纳-哈默斯坦模型中的两个线性子系统以及非线性子系统的参数均可以分别进行辨识估计,算法简单,数据量小.大量仿真结果表明,本文所提出的辨识方法是正确有效的.     

不完全信息下导弹参数与状态的联合估计

基于UKF推导了非线性系统参数的递推最大似然估计算法, 并结合UKF实现了对不完全信息下导弹的参数与状态的实时联合估计。首先通过引入导弹的导引律, 建立导弹的状态滤波模型, 进而给出参数辨识的一般递推似然法, 并在UKF状态滤波算法的基础上推导出非线性递推最大似然参数估计方法, 实现了参数估计与状态滤波的并行计算。仿真结果表明, 该方法收敛速度快, 具有很好的实时性和较高的估计精度。     

Fast state-space decimator with very low round-off noise

A method for the efficient realization of high-order Infinite Impulse Response (IIR) output-decimating State-Space Digital Filters (SSDF) is presented. By applying the state decimation and block processing to the nth-order narrow-band SSDFs, computational efficiency as well as low round-off noise levels are achieved. Further, the favorable behavior of certain SSDF forms with respect to the overflow limit cycles is retained. Such Fast State-Space IIR Decimators (FSSD) are simple to design, have multiplication rates comparable to Finite Impulse Response (FIR) decimators, have a highly parallel processing algorithm, and are very easy to implement on programmable signal processors. In the digital filter applications which do not require the linear-phase response, the FSSD may be considered as an alternative to a single-stage FIR decimator for moderate decimation factors.     

Time-varying parametric modeling of speech

For linear predictive coding (LPC) of speech, the speech waveform is modeled as the output of an all-pole filter. The waveform is divided into many short intervals (10–30 msec) during which the speech signal is assumed to be stationary. For each interval the constant coefficients of the all-pole filter are estimated by linear prediction by minimizing a squared prediction error criterion. This paper investigates a modification of LPC, called time-varying LPC, which can be used to analyze nonstationary speech signals. In this method, each coefficient of the all-pole filter is allowed to be time-varying by assuming it is a linear combination of a set of known time functions. The coefficients of the linear combination of functions are obtained by the same least squares error technique used by the LPC. Methods are developed for measuring and assessing the performance of time-varying LPC and results are given from the time-varying LPC analysis of both synthetic and real speech.     

Hierarchical statistical analysis of complex analog and mixed-signal systems

With increasing process parameter variations in nanometre regime, circuits and systems encounter significant performance variations and therefore statistical analysis has become increasingly important. For complex analog and mixed-signal circuits and systems, efficient yet accurate statistical analysis has been a challenge mainly due to significant simulation and modelling time. In the past years, there have been various approaches proposed for statistical analysis of analog and mixed-signal circuits. A recent work is reported to address statistical analysis for continuous-time Delta-Sigma modulators. In this article, we generalise that method and present a hierarchical method for efficient statistical analysis of complex analog and mixed-signal circuits while maintaining reasonable accuracy. At circuit level, we use the response surface modelling method to extract quadratic models of circuit-level performance parameters in terms of process parameters. Then at system level, we use behavioural models and apply the Monte-Carlo method for statistical evaluation of system performance parameters. We illustrate and validate the method on a continuous-time Delta–Sigma modulator and an analog filter.