Unbiased estimates for on-line system identification

An algorithm is derived for the determination of several unbiased estimates of the parameter vector of a discrete-time system, even in the presence of considerable noise, and requiring only one matrix inversion. From these noisy estimates an improved estimate can then be used by using stochastic approximation.     

单参数统一混沌系统的自适应控制同步

研究了单参数统一混沌系统的同步问题,提出了参数自适应控制同步方法;通过构造适当的控制函数和设计参数的自适应控制律,实现了两个不同参数的统一混沌系统的同步;根据Lyapunov稳定性定理,导出了两不同参数混沌系统能实现同步的充分条件,无论驱动系统处于何种状态,该方法都可使响应系统按照驱动系统给定的轨道演化。数值仿真表明了该方法的有效性。     

Continuous-time system identification on compact parameter sets

The problem of consistent estimation of the unknown parameters of linear time-invariant continuous-time systems is considered. The unknown parameter set is assumed to be compact, and only noisy observations of the system output are available. Sufficient conditions are derived for global strong consistency of the maximum-likelihood parameter estimates. The consistency proof exploits a recent result due to Delchamps concerning smoothness of the algebraic Riccati equation in the system parameters. This smoothness result has been tacitly assumed in the literature on local consistency of the parameter estimates. The previous global consistency results have been obtained under the restrictive assumption of finite parameter sets.     

Adaptive algorithms for sparse system identification

In this paper, identification of sparse linear and nonlinear systems is considered via compressive sensing methods. Efficient algorithms are developed based on Kalman filtering and Expectation-Maximization. The proposed algorithms are applied to linear and nonlinear channels which are represented by sparse Volterra models and incorporate the effect of power amplifiers. Simulation studies confirm significant performance gains in comparison to conventional non-sparse methods.     

在线连续交互式英语语音智能识别系统设计

英语语音智能识别系统的发展方向是在线、连续性的人机交互,为此,设计在线连续交互式英语语音智能识别系统,并基于梅尔频率倒谱系数设计语音识别函数。系统的参考数据库选取NOSE算法计算语音评价分数并查错,利用专家知识库纠正用户英语语音。系统由语音训练模块和在线连续交互式识别模块组成,前者为系统数据库提供英语语音的语料、音标资源,后者实施系统数据库的评价、纠正工作。实验结果证明设计的系统语音识别率高且识别时间短,有效性强。     

参数未知时滞混沌系统的自适应同步

考虑参数不确定的多维时滞混沌系统作为驱动系统(发射系统),系统的未知参数能够在系统中线性表示.采用参数辨识与自适应技术,在响应系统(接收系统)中应用线性反馈与参数自适应控制,使得驱动系统与响应系统能够混沌同步,并且响应系统的参数逐步逼近驱动系统的参数.利用Lyapunov泛函与最大不变集原理(Lassel)给出了理论分析与证明.理论分析与数值模拟结果表明该自适应控制同步策略的有效性.     

粒子滤波的机载激光通信自适应参数辨识跟踪方法

机载空间激光通信是实现未来超大容量空间通信的主要途径,机载空间激光通信终端的高精度实时动态跟踪一直是其研究的难点问题。为了解决机载空间激光通信终端的机动形式多样性的问题,提出了自适应参数辨识粒子滤波方法。在连续的时间域中,基于三阶线性微分方程的参数辨识模型描述机载空间激光通信终端运动,该模型能适应机载激光通信终端的多种运动模式,粒子滤波能处理非线性/线性高斯问题,因此可以引入到参数辨识模型中。实验结果表明:该算法能改善收敛精度,对工程应用也有重要意义。     

时滞时变对象参数辨识方法

针对时滞时变系统,提出了一种参数辨识的新方法.该算法一方面采用互相关函数来辨识滞后时间,并引进了快速傅里叶变换及其反变换,提高了计算效率;另一方面,在变参数增量估计递推最小二乘算法估计时变参数的基础上,引入误差级序列,改善了时变参数的辨识精度.最后通过仿真验证了该方法的良好性能.     

Intelligent quality controllers for on-line parameter design

Parameter design methods, in general, do not take into account the common occurrence that some of the uncontrollable factors are observable for products and processes, during operation and production, respectively. This paper introduces a methodology that facilitates on-line parameter design for products and processes utilizing the extra information available about observable uncontrollable factors. Implementation of the proposed methodology leads to a quality controller that operates in two distinct modes: identification mode and on-line parameter design mode, identification mode involves establishing a model that relates quality response characteristics with significant controllable and uncontrollable variables. On-line parameter design mode involves optimization of the controllable variables with respect to desired levels of output quality parameters, with consideration to levels of the observable uncontrollable variables. A plasma etching semiconductor manufacturing process is used as a testbed for the proposed intelligent quality controllers. Results reveal that the proposed quality controllers can be used for on-line parameter design of manufacturing processes. Results also reveal that significant improvements in quality (measured in terms of average deviation of process outputs from target) over off-line parameter design approaches are to be expected in production processes with some level of control on uncontrollable variables. Even in the absence of any control on uncontrollable variables, the proposed controllers always perform better than traditional off-line robust parameter design techniques; however, the improvements may not be significant     

Adaptive system identification using robust LMS/F algorithm

Adaptive system identification (ASI) problems have attracted both academic and industrial attentions for a long time. As one of the classical approaches for ASI, performance of least mean square (LMS) is unstable in low signal‐to‐noise ratio (SNR) region. On the contrary, least mean fourth (LMF) algorithm is difficult to implement in practical system because of its high computational complexity in high SNR region, and hence it is usually neglected by researchers. In this paper, we propose an effective approach to identify unknown system adaptively by using combined LMS and LMF algorithms in different SNR regions. Experiment‐based parameter selection is established to optimize the performance as well as to keep the low computational complexity. Copyright © 2013 John Wiley & Sons, Ltd.     

基于自适应同步的混沌系统参数辨识方法的研究

利用混沌系统的动力学特性对参数极其敏感的特点,以驱动响应同步结构为框架,设计了与同步误差有关的二次型指标函数,采用优化方法对参数进行自适应调节,直到同步误差最小,即实现混沌同步.该方法实现了参数未知混沌系统的参数辨识和自适应同步.为了研究参数收敛速度,对准高斯牛顿法、Hook-Jeeves方法(又名模式搜索法)和共轭梯度法等三种优化方法进行了对比研究.对具有两个未知参数的离散Hénon系统和连续Lorenz系统的仿真研究表明,基于共轭梯度的参数调节方法参数收敛速度最快,可以应用于混沌保密通信的解密.     

Sensorless control of permanent-magnet synchronous motors using online parameter identification based on system identification theory

An online parameter identification method and sensorless control using identified parameters were realized in surface and interior permanent-magnet synchronous motors (SPMSMs and IPMSMs, respectively). As this method does not use rotor position or velocity to identify motor parameters, the identified parameters are not affected by position estimation error under sensorless control. The proposed method can be applied to all kinds of synchronous motors. The effectiveness of the proposed method was verified by experiments in both SPMSMs and IPMSMs.     

Fault tolerant real time control system for steer-by-wire electro-hydraulic systems

A steer-by-wire (SBW) control system is presented with emphasis on safety issues. The applications are in articulated vehicles such as the wheel type loaders, articulated trucks, and others. The electro-hydraulic (EH) power circuit is controlled by two embedded electronic control modules (ECM), the primary ECM and backup ECM. The two ECMs monitor each others condition. If one detects fault in the other, it takes over the control functions. There are two main control algorithms that run in the ECMs in real-time: the steering valve control algorithm and the failure detection algorithm. The valve control algorithm basically generates command signal to the steering valve based on operator steering column signal as well as other machine condition sensors.The failure detection algorithm implements a fault detection logic for both input sensors and output drivers, and flags the corresponding warning for to the operator, and take a predefined action depending on the type of the failure detected. A unique fault strategy organization is implemented by inspecting the failure behavior on both the component and the system levels. The failure detection algorithm also determines the most likely “good” sensor signal from a set of redundant sensors for each critical measurement. Based on these good sensors data, the steering control algorithm sends two output signals: the control signal to the steering EH circuit valve and the control signal to the steering wheel force feedback device (i.e. a brake or a motor) to give operator feedback about the steering conditions.Finite state machine (FSM) concept is used to design the fault handling algorithms for both the component level and the system level failure. The probability of the system being at normal steering state or at any other steering failure state is determined. Failure mode probabilities of steering system components are also determined.     

光电跟踪伺服系统的自适应差分进化算法辨识

为了获得准确的光电跟踪伺服系统的模型,采用自适应差分进化算法对光电跟踪伺服系统进行辨识研究,该算法根据辨识误差平方和自动调整变异、交叉因子。在输入为正弦离散数字信号下辨识系统的离散模型。为了验证算法的有效性,在频域内与扫频法辨识的一、二阶模型和系统实际输出比较研究。实验结果表明:在相同正弦离散信号条件下,辨识输出与系统实际输出基本一致,与扫频法的RMSE相比减小了20.33%,二阶模型在高频段偏离系统实际输出稍大些,一阶系统输出与系统实际输出基本一致。研究结果表明,自适应差分进化算法计算量小,方法简便,辨识准确,在光电跟踪伺服控制领域具有一定的工程应用价值。     

An on-line least-squares parameter estimator with finiteconvergence time

An online parameter-estimation algorithm is presented which has the following features: (1) it reduces to the linear least-squares (LS) estimator after a set of regression vectors that span the full dimension of the parameter space has been processed, and (2) when the regression model is linear in the parameters, the parameter and prediction errors converge to zero in finite time. Both continuous- and discrete-time versions of the algorithm are given     

Adaptive sparse system identification using normalized least mean fourth algorithm

Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high‐identification performance, especially in low signal‐to‐noise ratio regime. It was well known that least mean fourth (LMF) can achieve high‐identification performance by utilizing fourth‐order identification error updating rather than second‐order. However, the main drawback of LMF is its instability and it cannot be applied to adaptive sparse system identifications. In this paper, we propose a stable sparse normalized LMF algorithm to exploit the sparse structure information to improve identification performance. Its stability is shown to be equivalent to sparse NLMS type algorithm. Simulation results show that the proposed normalized LMF algorithm can achieve better identification performance than sparse NLMS one. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive log domain filters for system identification using floating gate transistors

We describe an adaptive log domain filter with integrated learning rules for model reference estimation. The system is a first-order low pass filter implemented using multiple input floating gate transistors operating in subthreshold to realize on-line learning of gain and cut-off frequency. We use adaptive dynamical system theory to derive robust control laws for gain and cut-off frequency adaptation in a system identification task. Simulation results show that convergence is slower using simplified control laws but still occurs within milliseconds. Experimental results confirm that the estimated gain and cut-off frequency track the parameters of the reference filter. The adaptive log domain filter has measured power consumption of 33 μW. During operation, deterministic errors are introduced by mismatch within the analog circuit implementation. An analysis is presented which attributes the errors to current mirror mismatch.     

气体浓度在线检测系统的设计

快速、实时检测甲烷的产生及其浓度,对于工矿安全运行、人身安全、环境保护具有重要的意义.在比尔-朗伯定律的理论基础上,将吸收式光纤传感技术、差分检测技术及计算机数据处理技术相结合,设计了一套高精度的新型时间双光路差分气体浓度在线检测系统,可对气体浓度进行实时在线连续检测.采用计算机的快速数据处理功能,以具有强大软件开发能力的VisualC++6.0为工具,基于用户界面设计出该监测系统的实时动态监控软件.该系统提高了气体浓度检测灵敏度、实现了实时在线连续检测,数据的图形化显示,提高了系统的数据处理能力.     

在线考试系统的设计

文中系统为基于Java的在线考试系统。用户由学生、教师和系统管理员组成。学生登录系统可以进行在线测试和成绩查询。后台管理员可以对考题、考试设置信息、用户信息进行维护。学生、教师、管理员都可以对个人信息进行维护。将传统形式的考试电子化,基本上实现了传统意义上考试的所有功能流程。