一类双线性随机系统的辨识

本文对于一类具有规范形式的双线性、多变量、离散时间随机系统给出了确定结构指标和参数估计的递推方法及实现算法。首先导出了状态方程规范形等价的输入输出方程的一般形式,由此得到 Popov 规范形所对应的输入输出方程,然后借助于一、二阶相关     

双率系统极点配置自校正控制算法

针对控制输入频率是输出采样频率整数倍的双率系统,研究了极点配置自校正控制方法.由于双率采样数据系统存在未知的采样间输出(即损失输出),所以传统输入输出等周期单率系统极点配置自校正控制方法不适用于双率系统.为了解决这一困难,本文直接利用双率输入输出数据估算系统模型参数和采样间输出,进一步把估计的模型参数代入极点配置方程,通过求解多项式Diophantine方程.推导了被控系统控制律,给出了双率极点配置自校正控制算法.一个仿真例子说明双率系统极点配置算法的控制效果.     

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.     

多变量双线性系统的递推辨识及实现

本文对一类双线性系统给出了输入输出描述的一般形式。同时对于Popov选择路线下的规范形式,给出了辨识结构和参数的递推算法,以及简单易行的实现算法,这些算法可以很方便地在计算机上实现。     

基于标号变迁系统的测试集自动生成

首先,依据ISO89646的定义,阐述了协议一致性测试的基本概念,然后,介绍标号变迁系统（LTS）的形式化理论的定义和基本性质,利用LTS,给出测试例、测试集以及测试生成的形式化定义,第3,表述了实现关系在测试生成中的地位和作用,定义了输入输出系统,并在输入输出系统以及△变换的基础上引入实现关系ioco,根据实现关系ioco给出了一个测试集自动生成算法,该算法能很好地适用于递归的LTS。     

基于网点的自学习模糊逻辑系统

提出网点的概念与构造模糊逻辑系统的方法，给出了易于实现的学习算法。该方法适用于模糊规则难以获得而输入输出数据可得的情况，可用于设计基于样本的模糊控制器和系统模糊建模。理论分析和数字仿真说明了该系统的正确性与实用性。     

控制系统中一种新的周期性任务模型及其优化调度算法

分析了控制系统中的周期任务特性 ,给出了控制系统周期性任务的一种新任务模型 - -周期性任务分解模型 ,它将系统中的控制回路分解为几个子任务 .给出适合此任务模型的调度算法——双优先级调度算法 ,引入了辅助优先级 .该算法能够控制子任务执行顺序和降低控制输入输出延迟 .分析了任务集的可调度性 ,给出了任务集可调度的充分必要条件 .最后讨论了系统性能优化的问题 ,给出了系统性能优化的调度算法     

具有双面约束单点摩擦多体系统的数值计算方法

研究了一类具有双面约束单点摩擦的单自由度多体系统动力学方程的算法问题.首先给出了系统的动力学方程,该方程具有很强的非光滑性,不能应用已有的一些光滑系统的数值方法研究系统的动力学特性.因此,本文利用方程的特点和所求变量的物理含义,给出了一种简便的数值计算方法.该方法的计算效率和精度与迭代法相比均较高.     

基于GWO-BP方法的加速度计动态模型研究

针对高g值加速度计动态模型问题,基于Hopkinson杆的校准系统所测的输入输出数据建立系统模型,提出了GWO-BP神经网络动态建模方法。利用灰狼种群算法优化BP神经网络建立的加速度计动态模型,对模拟输入输出信号进行仿真。最后,利用Hopkinson杆标定系统对加速度计的输入输出进行实测。结果表明,相比于BP神经网络算法,该算法经过优化改进后,求解精度提高了43.6%,证明了该方法的可行性。     

一种非线性模型的在线辨识方法

提出一种用于非线性模型在线辨识的模糊算法。该算法将非线性输入输出系统用时变线性系统模型来拟和。并把此非线性系统模型表示成模糊模型的形式，用在线调节模糊模型的方法来辨识时变线性模型的相关参数。在以往的模糊辨识方法中，均未给出在线调整非线性系统的模糊辨识算法。将递推模糊聚类方法与卡尔曼滤波法用于在线调整模糊模型参数，仿真算例表明了此算法的有效性与良好的实用价值。     

Parameter identification of wiener models by multi-innovation algorithms

An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples.     

Minimal dimension realization and identifiability of input-output sequences

A necessary and sufficient condition of identifiability of an input-output sequence is given. An algorithm is presented by which a minimal dimension realization from a given input-output sequence can be obtained. A canonical input-output equation is introduced. It is shown that the state equation realization from a canonical input-output equation exists and is unique, up to an isomorphism.     

On-line frequency-and time-domain identification of a linear multivariable system

A method for the on-line identification of a linear multivariable plant subject to both deterministic and stochastic disturbances is proposed. The identification scheme rests on the use of a sum of sinusoids of distinct frequencies as probing-signal inputs and on the employment of linear time-varying filters to filter the plant inputs and the plant outputs. The time-varying filters are essentially banks of narrow-band filters tuned to the probing-signal frequencies. The filtered plant inputs and the filtered plant outputs yield an estimate of the plant transfer function matrix at the probing-signal frequencies. The filtered data are further processed using a recursive least-squares algorithm and a time-domain model estimate is obtained in terms of the coefficients of the difference equation relating each input-output pair. The identification algorithm is decoupled in the sense that the estimate of the transfer function or difference equation between the ith input and jth output is unaffected by other inputs or outputs. Simulation results on the performance of the time-varying filter and the identification scheme are given.     

系统辨识与建模的一种新方法

本文从函数逼近观点研究线性和非线性系统辨 识问题,导出辨识方程,提出用神经网络建立线性和非线性系统的模型．根据函数内差逼近 原理建立神经网络学习方程,给出优化算法．计算机仿真表明新算法计算速度快,具有良好 的推广、逼近和收敛特性．     

A cumulant based algorithm for the identification of input-output quadratic systems

The identification of a special class of polynomial models is pursued in this paper. In particular a parameter estimation algorithm is developed for the identification of an input-output quadratic model excited by a zero mean white Gaussian input and with the output corrupted by additive measurement noise. Input-output crosscumulants up to the fifth order are employed and the identification problem of the unknown model parameters is reduced to the solution of successive triangular linear systems of equations that are solved at each step of the algorithm. Simulation studies are carried out and the proposed methodology is compared with two least squares type identification algorithms, the output error method and a combination of the instrumental variables and the output error approach. The proposed cumulant based algorithm and the output error method are tested with real data produced by a robotic manipulator.     

一种新的基于神经模糊推理网络的复杂系统模糊辨识方法

针对基于输入输出数据的复杂系统的模糊辨识问题,提出了一种新的神经模糊推理网络及相应的学习算法．学习算法被应用于系统的结构辨识与参数辨识．在结构辨识阶段,介绍了一种新的直接从输入输出数据中抽取和优化模糊规则的学习算法;在参数辨识阶段,提出和推导了一种非监督学习和监督学习相结合的混合式学习算法,实现模糊隶属函数的初步调整和优化．仿真结果表明,本文的方法可以同时满足对辨识精度、收敛速度、可读性和规则数的要求．     

Incorporating prior knowledge in fuzzy model identification

This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that, when the proposed identification algorithm is applied, not only are physically justified models obtained but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.     

Experiences of system identification applied to ship steering

Different system identification methods have been applied to determine ship steering dynamics from full-scale experiments. The techniques used include output error, maximum likelihood and more general prediction error methods. Different model structures have been investigated ranging from input-output models in difference equation form to the equations of motion in their natural form. Effects of disturbances, errors and dynamics in sensors and actuators have been considered. Programs for interactive system identification have been used extensively. The experiments have been performed both under open loop and closed loop conditions. Both linear and nonlinear models have been considered. The paper summarizes the experiences obtained from applying system identification methods to many different ships. The results have been applied both to investigate steering properties and to design autopilots for ship steering. Insight into ship steering dynamics and identification methodology has been obtained.     

A hybrid clustering and gradient descent approach for fuzzymodeling

In this paper, a hybrid clustering and gradient descent approach is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed approach is composed of two steps: structure identification and parameter identification. In the process of structure identification, a clustering method is proposed to provide a systematic procedure to determine the number of fuzzy rules and construct an initial fuzzy model from the given input-output data. In the process of parameter identification, the gradient descent method is used to tune the parameters of the constructed fuzzy model to obtain a more precise fuzzy model from the given input-output data. Finally, two examples of nonlinear system are given to illustrate the effectiveness of the proposed approach.