基于行列式比的阶次和参数同步递推辨识算法

针对输出误差模型,结合辅助模型的思想对原有阶次辨识和参数估计的方法进行融合和扩展,推导出基于辅助模型的行列式比定阶法,同时得出模型的阶次和参数,不仅减少了辨识过程的计算量,也节约了辨识时间。考虑到原有行列式比定阶法可能存在的不准确性,提出了一种系统模型的确认方法,增强了阶次辨识能力。仿真实验也充分表明,对行列式比定阶法的扩展不仅可以准确地辨识出系统的阶次,得出的参数估计值也有较高的精度。     

基于行列式比的阶次和参数同步递推辨识算法

针对输出误差模型,结合辅助模型的思想对原有阶次辨识和参数估计的方法进行融合和扩展,推导出基于辅助模型的行列式比定阶法,同时得出模型的阶次和参数,不仅减少了辨识过程的计算量,也节约了辨识时间。考虑到原有行列式比定阶法可能存在的不准确性,提出了一种系统模型的确认方法,增强了阶次辨识能力。仿真实验也充分表明,对行列式比定阶法的扩展不仅可以准确地辨识出系统的阶次,得出的参数估计值也有较高的精度。     

基于输出误差模型优化的甲板运动预报算法研究

本文提出了一种适用于多种复杂海况的大型舰船甲板运动预报方法,目的在于提高算法对不同海域复杂海况的适用性,以及对甲板运动模型的辨识精度与预报精度。该方法通过将量测数据的时间滞后处理引入输出误差模型来描述甲板运动的动力学模型,引入定阶准则确定了模型最优阶数数对。在此基础上应用了辅助模型递推最小二乘算法进行系统参数辨识并估计输出误差模型中的状态变量。实验结果表明,本文所提出的预报方法在系统参数辨识阶段可以将递推最小二乘算法的辨识精度提高5.13%,并且在预报阶段可以有效地将甲板运动的幅值与相位预测精度提高3.17%。该方法在复杂海况下具备良好的预测性能,适用于大型舰船甲板运动预报。     

一类Wiener系统的动态环节的阶次辨识算法

Wiener系统的结构特点,使得其线性动态环节的输出不可测量,导致现有方法无法直接用于Wiener非线性系统的线性环节的定阶。为了准确确定一类Wiener系统包含的FIR函数的阶次,提出了一种残差曲线斜率法。该方法基于残差曲线的斜率来获得FIR的阶次。并且,该方法在不进行参数辨识的基础上,仅利用测量到的输入输出输出数据,来获得FIR的阶次。该算法既减小了计算量,也提高了定阶准确度。数值仿真验证了算法的有效性。     

小型无人驾驶直升机动力学模型辨识仿真研究

获得足够精确的动力学模型．对于小型无人驾驶直升机这种MIMO控制系统研究具有重要的意义。文中研究了直升机模型辨识方法,将子空间辨识算法运用于直升机动力学模型的辨识,并对模型直升机的垂直——偏航耦舍运动方程进行了辨识,得到了较为精确的结果,通过和PEM算法进行比较,显示该算法适合模型直升机系统辨识。     

城市就业人口的一种模型

本文从控制的观点,建立了城市基本就业人口和总就业人口间的关系的数学模型;提出一种参数辨识方法,它解决了小样本建模中定阶困难的问题。理论分析和实际案例均表明,所建模型较静态相关分析有更高的精度。     

一种基于气温因素的短期电力负荷预测模型北大核心CSCD

气温波动是影响短期电力负荷预测准确率的主要因素之一,为了提高预测精度,将气温因素引入一种新型的非线性自回归模型中,构建一种基于气温因素的非线性自回归短期电力负荷预测模型,并提出该模型的实验定阶方法。以气温作为模型的外部输入量,基于Weierstrass定理推导了该模型的表达式,采用最小二乘法估计该模型的参数,根据所提出的实验定阶方法对模型进行定阶。对实际电力负荷样本进行预测,结果验证了模型实验定阶方法的可行性,表明该负荷预测模型预测精度较高,可应用于负荷短期预测之中。     

短记忆性时间序列的建模分析及预测

AR模型作为时间序列模型的一种,由于其参数估计和定阶简单而广泛用于系统辨识.在多维AR序列的最小二乘建模的基础上,结合Kalman滤波算法,推导了应用Kalman滤波技术的多维AR序列参数估计方法.该算法无需保存历史数据,可对AR模型的估计参数进行实时的修正.在确定AR模型阶数时,提出了两步F检验法.选取上证某A股收益序列作为样本,利用时间序列相关性分析对该算法的有效性进行验证;对时间序列的RMSE和MAD指标进行比较,结果表明该算法大大减少了建模过程中的计算工作量,并具有较好的预测性.     

非线性倒立摆系统的神经网络辨识

基于四种非线性系统模型,对典型的不稳定、非线性、强耦合的倒立摆系统建立了神经网络辨识结构,并对辨识结果进行了简单的比较,讨论了隐含元个数的选择对模型辨识精度的影响。结果表明神经网络对于非线性倒立摆系统的辨识是有效的。     

基于遗传算法的舵机数学模型辨识研究

针对"飞思卡尔"杯全国大学生智能车竞赛,在智能车整体控制中,对于舵机的精准控制显得十分重要;分析了舵机系统各个部分组成原理,采用实验建模方法,利用实验测得的舵机系统的阶跃响应曲线来辨识出舵机模型参数(增益k、惯性时间常数T、滞后时间L);并根据遗传算法原理,借助MATLAB软件方便的绘图功能和强大的图像处理能力,用MATLAB语言编写了M文件进行辨识仿真;仿真结果表明,辨识参数误差小,对于舵机模型是一种有效的辨识方法;实践证明了该方案的可行性,这为实现舵机实时准确控制提供了重要的理论支持。     

一种多变量过程辨识方法及其实现

分析讨论了在Ｍａｒｋｏｖ参数估计的基础上，通过ＦＩＲ—ＭＰＳＳＭ—ＳＳ变换，实现多变量过程的参数模型辨识的问题，并给出研究实例。该方法回避了多变量过程结构辨识的困难，是工业过程建模的良好途径。     

群体智能算法总体模式的形式化研究

在对群体智能算法给出形式化定义的基础上 ,给出了群体智能算法的总体模式框图 ,并以微粒群优化算法实例进行了具体算法模式的形式化描述 .     

System identification for generic model control

System identification methods build mathematical models of dynamical systems based on observed data. The intended use of the model should always be reflected in the methods and techniques used for identification. In this paper an identification scheme is derived for the case where the model is going to be used for GMC controller design. The aim of GMC control is to make the output approach a setpoint along a given desired trajectory. This is reflected in the identification scheme which is non-standard in two ways. Firstly, the emphasis is on the output trajectories of the models, and secondly we try to make the prediction errors follow an error trajectory determined by the controller parameters. Simulation studies are included which show that the derived identification scheme performs well.     

基于温备份技术的高可靠嵌入式控制器设计

介绍了面向空间科学实验应用背景的高可靠控制器的解决方案。该方案利用MSP430与IGLOO系列FPGA的低功耗模式,设计了一种新型的温备份方法,在保证系统高可靠性的前提下依然能够达到较低的功耗,并确保科学实验在单路控制电路失效的情况下能够不中断实验进程。该控制器具有高可靠、低功耗以及接口资源丰富的特点,可以满足各类空间科学实验的需求,并已应用于空间有效载荷集成支持系统项目中。     

Residual models and stochastic realization in state-space identification

This paper presents theory and algorithms for validation in system identification of state-space models from finite input-output sequences in a subspace model identification framework. Our formulation includes the problem of rank-deficient residual covariance matrices, a case which is encountered in applications with mixed stochastic-deterministic input-output properties as well as for cases where outputs are linearly dependent. Similar to the case of prediction-error identification, it is shown that the resulting model can be decomposed into an input-output model and a stochastic innovations model. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs thereby avoiding several problems appearing in standard application of stochastic realization to the model validation problem.     

Interval Fuzzy Model Identification Using$l_infty$-Norm

In this paper, we present a new method of interval fuzzy model identification. The method combines a fuzzy identification methodology with some ideas from linear programming theory. We consider a finite set of measured data, and we use an optimality criterion that minimizes the maximum estimation error between the data and the proposed fuzzy model output. The idea is then extended to modeling the optimal lower and upper bound functions that define the band which contains all the measurement values. This results in lower and upper fuzzy models or a fuzzy model with a set of lower and upper parameters. The model is called the interval fuzzy model (INFUMO). We also showed that the proposed structure uniformly approximates the band of any nonlinear function. The interval fuzzy model identification is a methodology to approximate functions by taking into account a finite set of input and output measurements. This approach can also be used to compress information in the case of large amount of data and in the case of robust system identification. The method can be efficiently used in the case of the approximation of the nonlinear functions family. If the family is defined by a band containing the whole measurement set, the interval of parameters is obtained as the result. This is of great importance in the case of nonlinear circuits' modeling, especially when the parameters of the circuits vary within certain tolerance bands     

A fault-tolerant control strategy for non-linear discrete-time systems: application to the twin-rotor system

In this paper, an active fault-tolerant control scheme is proposed in the case of actuator faults. In particular, the general idea of integrating fault identification and control schemes, which takes into account the fault estimation error is first presented in a linear context. As a result, the so-called separation principle for the controller and the fault identification scheme is developed. Subsequently, the proposed approach is extended to a class of non-linear systems. Similarly to the linear case, it is proven that using a suitable control strategy and a faulty identification scheme it is possible to obtain an integrated fault-tolerant control framework, which takes into account the fault identification error. As a result, a non-linear counterpart of the above-mentioned separation principle is developed. Finally, the last part of the paper shows the application results obtained using a twin-rotor system that confirm the high performance of the proposed approach.     

面向持续集成的回归测试优化方法

针对持续集成环境下回归测试需要进行持续优化的问题,提出一种依据回归测试目标自适应调整策略的优化方法.首先将失败标志、缺陷检测数、重要性因子、新旧功能标志作为用例属性进行标记,根据历史数据和关联关系对用例属性进行初始化;而后根据阶段测试目标,区分新功能测试、修改性测试,将需求映射为具体的用例属性指标,据此对用例进行选择;计算重要性因子,更新用例属性标签,根据用例属性进行优先级自动排序;用例执行中,按照时间、资源要求,根据用例属性选择相应规模的测试用例进行执行.最后选择开源数据集进行实验,结果表明该方法针对不同的测试目标均能够降低执行用例的规模,提高缺陷检测效率.     

Identification and Control of Multivariable Stochastic Feedback Systems

Abstract

This paper considers the problem of simultaneous identification and control of stochastic processes characterized by linear dynamic models with unknown systems parameter coefficients. Stochastic approximation is used to derive consistent identification algorithms for the case in which arbitrary feedback controls are present. These identification methods can also be used for determining the order of the system, if the latter is unknown, as well as the exact canonical structure for the multivariable case.

An approximation to the optimal control solution is obtained by explicitly separating the functions of identification and control, and asymptotic convergence to a stochastic optimal controller is attained without on-line structural modification.     

Frequency domain identification of continuous-time output error models, Part I: Uniformly sampled data and frequency function approximation

This paper treats several aspects relevant to identification of continuous-time output error (OE) models based on sampled data. The exact method for doing this is well known both for data given in the time and frequency domains. This approach becomes somewhat complex, especially for non-uniformly sampled data. We study various ways to approximate the exact method for reasonably fast sampling. While an objective is to gain insights into the non-uniform sampling case, this paper only gives explicit results for uniform sampling.