惯性系统的时域在线辨识

给出二阶惯性系统阶跃响应斜率和幅值在幅值拐点处的时域表达式及其矩形脉冲响应幅值在其极大值点处的时域表达式,三阶惯性系统阶跃响应斜率在响应的二阶导数极大值点处、阶跃响应幅值在其拐点处和矩形脉冲响应斜率在响应二阶导数极小值点处的时域表达式,并找到了一种在时域辨识一阶、二阶和三阶惯性系统的方法．实测结果表明,该方法简便、可靠,测试精度高．

     

基于遗传算法的系统在线辨识

首先介绍了一种基于自然选择和自然遗传学机理的全局搜索学习算法－遗传算法，并讨论了它在系统在线辨识中的实现，仿真表明，遗传算法能在线辨识时滞并收敛到全局最优。     

分数阶系统时域子空间辨识

研究了分数阶系统的时域辨识问题,给出了一种新的分数阶系统时域子空间辨识算法.当分数阶微分阶次已知时,通过计算输入输出信号的分数阶微分,构造新的输入输出数据方程对系统的参数进行子空间辨识.当分数阶微分阶次未知时,通过代价函数将阶次辨识问题转化为参数寻优问题.采用Poisson滤波器有效避免了在计算分数阶微分时输入输出信号必须高阶可导的问题.通过分析给出了权矩阵的选取方式,提高了时域子空间辨识结果的精度.数值仿真结果表明了该算法的有效性.     

模型在线辨识方法及其应用

本文提出了一种有效的非线性模型和参数在线估计方法。为了实现模型在线辨识，本文根据误差性能指标，给出了模型判据及计算式。根据递推加权最小二乘算法和优选判据，导出了模型和参数同时在线估计的有效算法。为了提高计算效率和数值稳定性，模型辨识和参数辨识均采用了Ｕ－Ｄ分解方法。新方法可用于飞行器非线性气动模型和参数的实时估计。实际应用结果表明，使用该方法可以有效地确定多项式、样条函数模型结构，参数辨识的结果满     

极值系统在线辨识与稳态优化控制方法研究

研究一种基于非线性环节直接表示的ＳＩＳＯ Ｈａｍｍｅｒｓｔｅｉｎ模型参数辨识方法；针对非线性环节呈极值特性的情况，研究一种由在线辨识得到的非线性环节的参数估计值进行稳态优化的控制方法，仿真结果验证了该方法的有效性。     

用模糊模型在线辨识非线性系统

讨论用模糊方法实现非线性系统在线辨识问题.首先给出了简化的模糊规则表达 方法及其相应的自适应模糊推理,在此基础上给出了模糊模型参数在线辨识算法.最后对 非线性模型进行在线辨识,验证了本文提出的模糊模型及其在线辨识算法.     

基于混合遗传算法的时滞和参数的在线辨识

针对遗传算法应用于时变时滞和参数在线辨识时，存在无法兼顾收敛速度与辨识精度的缺点，提出一种遗传算法和单神经元有机结合的混合遗传算法。利用GA的全局最优性在整个空间搜索可能的极值，而用单神经元的误差梯度下降特性在极值点附近快速搜索，从而达到全局最优与快速搜索的有机结合，提高了收敛速度和辨识精度。并对混合遗传算法进行了改进，使之更适用于在线辨识。仿真结果表明，改进的混合遗传算法用于在线辨识有效且实用。     

基于LabVIEW变风量空调系统在线辨识建模

将虚拟仪器技术应用于系统辨识中,根据相关辨识理论,结合西安建筑科技大学变风量空调实验室为研究平台,采用图形化的设计语言LabVIEW设计了在线系统辨识。研究了变风量空调系统中频率与送风风量的在线辨识建模,利用该平台所作实验、分析结果证明了实验研究的一种实用有效的工具,并且能推广到系统辨识的其他领域。     

基于在线辨识的极值系统动态寻优控制方法研究

本文介绍一种非线性环节采用折线近似表示的Ｈａｍｍｅｒｓｔｅｉｎ模型系统的参数辨识方法；针对非线性环节呈极值特性的情况，研究一根据在线辨识得到的线性动态环节的参数估计值进行级值系统动态优化的控制方法，仿真结果验证了该方法的有效性。     

一类高阶次随机非线性系统的输出反馈镇定

针对一类高阶次随机非线性系统,研究其输出反馈镇定问题．通过选择有效的观测器和李雅普诺夫函数,所设计的光滑输出反馈控制器保证了闭环系统的平衡点是依概率全局渐近稳定的,输出几乎处处调节到零．数值仿真验证了控制方案的有效性．     

On-line terminal response time: The effects of background activity

This paper reports on a set of experiments to determine the effects on the response time of an on-line system when different typs of jobs run in the background partition. Since many applications may be better served by an on-line system but do not justify a dedicated system, it is frequently necessary to run batch jobs in the background partition of a computer supporting an on-line system. Substantial differences in terminal response time were observed depending on what jobs were scheduled in the background partitions. The computer manager should be aware of these factors when scheduling such batch work.     

Indirect approach to continuous time system identification of food extruder

A three-stage approach to system identification in the continuous time is presented which is appropriate for day-to-day application by plant engineers in the process industry. The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response. The method is evaluated on a pilot-scale food-cooking extruder.     

频域和时域船舶动力定位参数辨识算法比较

针对频域方法和时域方法均被用于船舶动力定位系统建模,但未见特点和适用性对比研究的问题,以船舶动力定位系统参数辨识为对象,进行了频域法R-MISO (reverse multiple input-single output)和时域算法EKF (extended kalmanfilter)的算法实现难易程度、收敛速度、参数依赖性、抗噪能力和适应场合等的比较;基于MATLAB仿真结果显示RMISO算法辨识时间短,不需要设置初始值,适用于在线辨识,而EKF算法去噪能力比较强,需要数据量大,结果依赖初始值,适用于离线辨识,从而为实际船舶动力定位系统参数辨识提供一定的指导.     

Nonlinear system identification using optimized dynamic neural network

In this paper, both off-line architecture optimization and on-line adaptation have been developed for a dynamic neural network (DNN) in nonlinear system identification. In the off-line architecture optimization, a new effective encoding scheme—Direct Matrix Mapping Encoding (DMME) method is proposed to represent the structure of neural network by establishing connection matrices. A series of GA operations are applied to the connection matrices to find the optimal number of neurons on each hidden layer and interconnection between two neighboring layers of DNN. The hybrid training is adopted to evolve the architecture, and to tune the weights and input delays of DNN by combining GA with the modified adaptation laws. The modified adaptation laws are subsequently used to tune the input time delays, weights and linear parameters in the optimized DNN-based model in on-line nonlinear system identification. The effectiveness of the architecture optimization and adaptation is extensively tested by means of two nonlinear system identification examples.     

用MATLAB求取线性系统的时域性能指标

采用MATLAB编程求取线性系统的时域性能指标,用户只需从键盘上输入线性系统的传递函数的分子,分母多项式系数,就可以获得该系统的时域性能指标和阶跃响应图,使得时域性能指标的求取变得简单、容易、准确、应用实例验证了所设计的程序的有效性,该程序可用于线性系统的分析和设计。     

Frequency domain versus time domain methods in system identification

The basic techniques of time domain and frequency domain identification, including the maximum entropy methods, are outlined. Then connections and distinctions between the methods are explored. This includes the derivation of some analytic relationships together with a discussion of the restrictions inherent in choosing certain methods, and their ease of use in different experimental conditions. It is concluded that these are complementary rather than competing techniques.     

分数阶系统频域辨识算法

分数阶微积分提供了一个很好的工具来描述一些复杂的实际系统,比整数阶模型更简洁准确.针对分数阶系统建模问题,阐述了一种同元次分数阶系统频域辨识的极大似然算法.为此首先简要地介绍了同元次分数阶系统的传递函数表达形式,然后在此基础上推导了分数阶系统频域极大似然算法,利用拉格朗日法证明了似然函数和代价函数的等价性,从而将辨识问题归结为一等价的优化问题,并进一步对采用Gauss-Newton优化计算方法进行了讨论.最后通过仿真实例验证了其有效性.     

The people's time sharing system

A set of programs running under a multiprogramming batch operating system on the CDC 6600 which provide remote users with a time sharing service is described. The basis for the system is the ability of a user program to create job control statements during execution, thereby tricking the operating system into treating it as an ordinary batch job. The text editor and the interactive debugging facilities are described. The performance of the system, known as the People's Time Sharing System (PTSS), and user reaction to it are also described.     

On the equivalence of time and frequency domain maximum likelihood estimation

Maximum likelihood estimation has a rich history. It has been successfully applied to many problems including dynamical system identification. Different approaches have been proposed in the time and frequency domains. In this paper we discuss the relationship between these approaches and we establish conditions under which the different formulations are equivalent for finite length data. A key point in this context is how initial (and final) conditions are considered and how they are introduced in the likelihood function.     

On-line system identification in real time using a minicomputer

The parameters of the dynamic model of a linear system are estimated from the measurements of the input and output data by using the recursive algorithm for matrix pseudoinverse. For measurements contaminated with noise, the stochastic approximation algorithm is employed in conjunction with the matrix pseudoinverse algorithm for obtaining unbiased least-squares estimates. Both the methods are tested on-line in real time using the PDP 11/45 minicomputer while the system is simulated on a TR-20 analogue computer. The results confirm the feasibility of using the algorithms to identify the parameters of a class of industrial processes on-line using a minicomputer. This has special application to the adaptive control of such processes, based on rapid identification of slowly-varying parameters.