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.     

2 m望远镜消旋K镜转台的模型辨识

针对某2 m望远镜消旋K镜转台,提出了一种基于Hankel矩阵奇异值分解的特征系统实现算法对系统的参数和阶次进行辨识。首先,以正弦扫描信号激励转台并同步采集位置反馈信息,利用谱分析法对测试数据进行分析,得到了系统的频率特性曲线;其次,对系统的Hankel矩阵进行奇异值分解,得到了K镜转台的结构模型;最后,采用特征系统实现算法对Hankel矩阵进行辨识,得到了K镜转台的参数模型。实验结果显示:K镜转台相对均衡的最小阶阶次为6阶,在系统的中低频段获得幅度0.31 dB和相位0.87的辨识精度,相对于参数递阶辨识方法,分别提高了50.7%和23%。结果表明:该方法能够确定一个与系统外特性等价的相对均衡的最小阶状态空间模型,在辨识系统阶次和参数估计方面具有较好的可行性和实用性。     

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。     

自动化技术与远动技术

0005055区间系统的 H_∞鲁棒控制[刊]/吴方向//自动化学报.—1999,25(5).—705～708(C)0005056大系统的递阶辨识[刊]/丁锋//自动化学报.—1999,25(5).—647～654(C)大系统的特点是维数高、待估计的参数数目多,使得辨识方法的计算量和存储量急剧增加,以致常规辨识算法难以实现。为了减少大系统辨识的计算量,提出了计算量较小的递阶辨识算法,并用鞅超收敛定理证明了它的收敛性。结果说明该算法可以给出大系统参数的一致估计。参7     

人体头部过载模型阶次的辨识与降阶研究

针对在系统辨识过程中面临的人体头部过载模型阶次辨识问题,介绍了常用的模型阶次辨识方法及利用残差平方和即损失函数J估计模型阶次的原理,并基于20组试验数据开展人体头部过载模型阶次辨识研究。通过系统辨识获得了人体头部过载模型后,采用Gram阵对Hankel奇异值分解方法实施模型简化和降阶处理。通过比较降阶前后模型,表明模型降阶方法是有效和正确的。     

Box─Jenkins模型结构辨识算法

本文基于残差序列自相关检验原理和递推广义最小二乘估计，提出Box-Jenkins模型结构辨识算法，并可联立估计出系统各部分的子阶与时滞。文中给出了仿真结果。     

改进的PSO算法在非线性模型参数辨识中的应用

参数辨识是过程建模的基础,对于参数辨识问题提出了许多不同的方法.针对传统模型参数辩识方法和遗传算法用于模型参数辨识时的缺点,提出一种基于微粒群优化(PSO)算法的模型参数辨识方法,利用PSO算法的强大优化能力,通过对算法的改进,将过程模型的每个参数作为微粒群体中的一个微粒,利用微粒群体在参数空间进行高效并行的搜索,以获得过程模型的最佳参数值,并将其用于对非线性系统模型的参数辨识,可有效提高参数辨识的精度和效率.该方法应用到实际例子中,获得了满意的辨识精度和效率,得到较为精确的过程模型,模型输出与实际输出基本一致,仿真结果令人满意.实例仿真结果表明,微粒群算法为非线性系统模型参数辨识提供了一种有效的途径.     

着舰控制系统中的甲板运动补偿技术研究

舰载机在着舰中对甲板运动的跟踪存在相位差,需要使用预报技术将甲板的运动信息提前加入到自动着舰控制系统(ACLS)中.甲板预报通常采用的自回归模型法(AR)是一种时间序列预报方法,在此基础上提出了一种新型的多层递阶预报模型,它是在对时变参数进行预报的基础上再对系统的状态进行预报,更能反映时变系统的运动实质.对典型海况下甲板的俯仰和沉浮运动进行预报仿真,结果表明多层递阶预报的精度更高.分别用两种方法对自动着舰纵向轨迹控制进行运动补偿,仿真表明多层递阶预报方法可以更加有效地提高轨迹跟踪精度.     

快速反射镜状态模型构建方法及其控制系统设计

为减少快速反射镜状态空间建模过程中所需的结构参数数量,提出了一种基于系统辨识的状态模型构建方法,采用该方法建立状态模型时只需使用音圈电阻和电感两个结构参数.基于状态模型,设计了一套由降阶观测器、状态反馈、内模和镇定补偿器构成的组合控制系统,利用状态反馈完成对内模和镇定补偿器的设计,通过设计降阶观测器实现对电流和角速度的获取,组合系统可同时实现对输入信号的渐进跟踪和干扰的抑制.在SIMULINK中建立仿真模型,仿真结果显示,不考虑干扰作用时,相较于不完全微分PID(Proportion Integration Differentiation)控制系统,组合系统的调节时间下降了53.6％,超调量上升了131.2％;加入干扰信号后,不完全微分PID控制系统的动稳态性能有明显下降,而组合系统的输出性能基本不受影响.仿真结果验证了理论分析的正确性.     

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。     

Hierarchical identification of lifted state-space models for general dual-rate systems

This paper is motivated by practical consideration that the input updating and output sampling rates are often limited due to sensor and actuator speed constraints. In particular, for general dual-rate systems with different updating and sampling periods, we derive the lifted state-space models (mapping relations between available dual-rate input-output data), and, by using a hierarchical identification principle, present combined parameter and state estimation algorithms for identifying the canonical lifted models based on the given dual-rate input-output data, taking into account the causality constraints of the lifted systems. Finally, we give an illustrative example to indicate that the proposed algorithm is effective.     

Modified Subspace Identification for Periodically Non-uniformly Sampled Systems by Using the Lifting Technique

This paper studies identification problems for a class of multirate systems—non-uniformly sampled systems. The lifting technique is employed to handle the non-uniformly sampled input and output data, a lifted state-space model is derived to represent the non-uniform discrete-time systems, and a novel subspace identification method is proposed to deal with the casuality constraints in the lifted model. Simulation results show that the algorithm is effective.     

Optimal signal reconstruction in noisy filter bank systems:multirate Kalman synthesis filtering approach

A multirate Kalman synthesis filter is proposed in this paper to replace the conventional synthesis filters in a noisy filter bank system to achieve optimal reconstruction of the input signal. Based on an equivalent block representation of subband signals, a state-space model is introduced for an M-band filter bank system with subband noises. The composite effect of the input signal, analysis filter bank, decimators, and interpolators is represented by a multirate state-space model. The input signal is embedded in the state vector, and the corrupting noises in subband paths are generally considered as additive noises. Hence, the signal reconstruction problem in the M-band filter bank systems with subband noises becomes a state estimation procedure in the resultant multirate state-space model. The multirate Kalman filtering algorithm is then derived according to the multirate state-space model to achieve optimal signal reconstruction in noisy filter bank systems. Based on the optimal state estimation theory, the proposed multirate Kalman synthesis filter provides the minimum-variance reconstruction of the input signal. Two numerical examples are also included. The simulation results indicate that the performance improvement of signal reconstruction in noisy filter bank systems is remarkable     

Multirate Crosstalk Identification in xDSL Systems

Crosstalk between multiple services transmitting through the same telephone cable is the primary limitation to digital subscriber line (DSL) services. From a spectrum management point of view, it is important to have an accurate map of all the services that generate crosstalk into a given pair. This paper on crosstalk identification is motivated by an important practical consideration: the signals constituting the crosstalk are transmitted at different rates in xDSL systems. Therefore, we here propose to use the “blocking technique,” we derive blocked state-space models for multirate xDSL networks, and we set up the mapping relationship between available input and output data. Further, we use the least-squares principle to identify the crosstalk functions, and study the convergence rate and upper bound of the parameter-estimation error. Finally, we illustrate and verify the theoretical findings with simulation examples.     

基于自适应Kalman滤波的二维有噪子带信号恢复

基于子带信号的多通道表示（multichannel representation)和输入信号的动态特征,本文尝试推出了一种多分辨率状态空间模型,它与带相加子带噪声的滤波器组（Filter Bank)系统是等价的,于是使有噪子带信号的恢复可表述为相应多分辨率态空间模型的最优状态估计问题。进一步又利用信号的向量动态模型,发展了适于二维Kalman滤波的二维多分辨率状态空间模型,根据信号行为的分布,目标平面（object plane)可分割为不同的区域并用不同的向量动态模型来表征信号的非平衡分布,计算机数字仿真结果进一步证实了本文所提出了二维多分辨率Kalman滤波器性能的优越性。     

State-space description of a multirate digital filter

The concept of multirate sampling in a digital filter is discussed, and a generalised method using the state-space technique of describing and analysing a 2nd-order multirate digitalfilter is presented.     

The hierarchical timing pair model for multirate DSP applications

The problem of representing timing information associated with functions in a dataflow graph is considered. This information is used for constraint analysis during behavioral synthesis of appropriate architectures for implementing the graph. Conventional models for timing suffer from shortcomings that make it difficult to represent timing information in a hierarchical manner for sequential and multirate systems. Some of these shortcomings are identified, and an alternate timing model that does not have these problems for hardware implementations is provided. We introduce the concept of timing pairs to model delay elements in sequential and multirate circuits and show how this allows us to derive hierarchical timing information for complex circuits. The resulting compact representation of the timing information can be used to streamline system performance analysis. In addition, several analytical results that previously applied only to single rate systems can now be extended to multirate systems. We present an algorithm to compute the timing parameters and have used this to compute timing parameters for a number of benchmark circuits. The results obtained on several ISCAS benchmark circuits as well as several multirate dataflow graphs corresponding to useful signal processing applications are presented. These results show that the new representation model can result in large reductions in the amount of information required to represent timing for hierarchical systems.     

Joint Estimation of States and Parameters for an Input Nonlinear State-Space System with Colored Noise Using the Filtering Technique

This paper concerns the state and parameter estimation problem for an input nonlinear state-space system with colored noise. By using the data filtering and the over-parameterization technique, we transform the original nonlinear state-space system into two identification models with filtered states: one containing the system parameters and the other containing the noise model’s parameters. A combined state and parameter estimation algorithm is developed for identifying the state-space system. The key is that the estimation of system parameters uses the estimated states, and the estimation of states uses the preceding parameter estimates. A simulation example is provided to show that the proposed algorithm can work well.     

Hierarchical path hunt in a broadband multirate circuit switchingfabric

A circuit switching fabric that switches narrowband (DS0), wideband (N×DS0, N×VT), and broadband (STS-N) signals embedded in SONET streams is studied. Based on the Clos (1953) strictly nonblocking principle, the fabric is strictly nonblocking if all path hunts are performed at the basic-rate (DS0 in our case) level. On the other hand, a hierarchical path hunt can speed up the path hunt process for a high bit-rate call, though the fabric will then lose its nonblocking property. We study several hierarchical path hunt algorithms and investigate how the hierarchy impacts the blocking probability. We also study the performance impact resulting from doing broadcast/multicast in a multirate circuit switching fabric. Our results provide insights into how hierarchical path hunts should be designed in a broadband multirate circuit switching fabric     

Optimal reconstruction of ARMA signals with decimated samples under corrupting noise by use of multirate Kalman filter

A multirate modeling theory of the ARMA stochastic signals is derived from a state-space viewpoint in this work. Its application to the signal reconstruction problem for the recovery of the complete ARMA signal from its noise-corrupted, missing-sample sequence is then developed in detail. The proposed estimation-interpolation problem can be resolved by using the multirate optimal state estimation scheme of this work. Theoretically, the multirate Kalman reconstruction filters derived in this paper produce the minimum variance estimation and interpolation of the original complete, clean ARMA signal. Practically, the numerical examples show that the multirate Kalman reconstruction filters illustrate good estimation/interpolation performances, not only for synthetic ARMA sequences but also for human speech signals.