模型辨识新方法及应用

本文提出了一种有效的模型辨识新方法，为了提高数值稳定性和计算效率，本文给出了一种递阶最大信息量（ＡＩＣ）新判据和参数估计新方法，使单输出系统的极大似然方法及模型辨识的优选判据计算量成倍减少，通过分析量系统的ＡＩＣ标准，本文进一步导出了多输出情况下的ＡＩＣ标准，大大提高了计算效率。     

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     

含ARMA噪声系统模型的参数辨识方法*

实际问题中，大量的动态系统控制问题可归结为含ＭＡ，ＡＲＭＡ噪声系统模型的参数辨识问题。本文提出ＲＭＡ，ＲＡＲＭＡ两种系统模型参数辨识的一种新方法，主要手段是构造和研究特殊的辅助线性模型。理论分析和实际计算表明，本文方法较传统表度有明显提高。     

一种基于采样系统性质分析的连续系统传函辨识别新方法

本文研究了任一连续系统采样后的性质,提出了一种无需时频转换(FFT运算)而直接 求取连续系统传函模型的辨识新方法.它适用于在线或离线递推,计算简便、计算简便、精确性好,且很 容易推广到多输入多输出模型的辨识.     

非线性系统的参数空间分割辨识方法

本文提出了一种新的非线性系统Volterra级数模型辨识方法,为非线性系统辨识中 的“维数灾难”问题提供了一种满意的解决．算法中参数空间分割和模型辨识同时完成,降 维依据采用输出拟合结果的均方误差,最终得到输出拟合均方误差意义上的准最优解．本算 法也可以作为非线性系统模型的结构辨识算法,并可以直接推广应用于其它很大一类非线性 系统模型．仿真试验结果表明,算法计算量小,精度高,并具有较好的稳定性,可以应用于 在线实时辨识．     

飞机极曲线模型和参数鲁棒辨识方法

为了有效地确定飞机极曲线，提出一种鲁棒选择模型的新方法，通过分析数据矩阵模型判定方法，采用U—D分解以避免行列式的复杂计算，从而成倍提高了计算效率，通过估计D阵元素的取值区间，得到了观测量不确定部分带来的模型辨识判据的误差上下界，依此将候选按照重要程度逐个选取，在加权最小二乘算法中，采用下界不等式逼近，得到了鲁棒辨识的新算法和收敛条件，对飞机极曲线的模型和参数进行辨识，结果表明新方法可以得到工程上满意的效果。     

闭环系统的输出过采样辨识方法及其估计精度分析

传统闭环系统辨识方法的可辨识性受到参考设定信号和控制器结构的限制．提出了一种通过对输出过采样实现线性离散时间闭环系统辨识的方法，输出过采样提供了更多的系统结构信息，在传统辨识方法的可辨识条件不满足的情况下，仍能正确辨识系统参数，针对有色噪声干扰，分析其在不同过采样率下的估计精度，得出最优估计的过采样率计算方法．辨识方法实现简单、运算量小、估计精度高．仿真试验验证了其有效性．     

一种多输入多输出系统模型辨识方法及其应用

目前的辨识方法一般需要在系统输入端加入激励信号，而且多输入多输出系统的在线辨识仍很困难。本文提出一种基于牛顿迭代法的多输入、多输出对象模型迭代辨识方法，模型参数更新的依据是使模型预测输出与全部采样时刻的对象实际输出之间的均方差递减，直到收敛。这种基于全局数据迭代的辨识方法可进行闭环辨识，无需外加激励信号，适用于多输入多输出对象的在线辨识。对一个两输入、两输出对象模型的仿真研究和某电厂300MW机组负荷被控对象的计算结果表明，辨识效果令人满意。     

面向控制器设计的多变量系统辨识实验信号

给出一种当多交量系统辨识模型用于控制器设计时的开环实验输入信号．假定系统未楚模动态可以用加性不确定性表示，取实际输出和理想输出误差平方均值最小，将其与系统辨识最小二乘法相结合，得到一种最优输入信号的设计方法．因输入信号与控制性能的相关性，可获得比普通随机信号更好的辨识结果．仿真结果证明了该方法的有效性．     

一种基于过程神经元网络的非线性动态系统辨识模型及应用

针对复杂非线性动态系统辨识问题,提出了一种基于过程神经元网络（PNN）的辨识模型和方法．根 据系统待辨识的模型结构和反映系统模态变化特征的动态样本数据,利用PNN 对时变输入／输出信号的非线性变 换机制和自适应学习能力,建立基于PNN 的系统辨识模型．辨识模型能够同时反映多输入时变信号的空间加权聚 合以及阶段时间效应累积结果,直接实现非线性系统输入／输出之间的动态映射关系．文中构建了用于并联结构和 串－并联结构辨识的PNN 模型,给出了相应的学习算法和实现机制,实验结果验证了模型和算法的有效性．     

基于遗传规划的哈默斯坦模型的辨识

该文基于遗传规划提出了一种辨识哈默斯坦模型的新方法。哈默斯坦模型由静态非线性模块和动态线性模块串联而成,因此系统辨识的目标是要找到非线性和线性模块的最优数学模型。该文通过遗传规划确定非线性模块的函数结构,并结合遗传算法确定模型的未知参数,适应度值的计算采用了最小信息量准则(A IC),以平衡模型的复杂度和精确度。该方法不需要对模型的先验知识有详细了解,就能达到较好的辨识效果,并且能够克服观测噪声的污染,获得参数的无偏估计。仿真结果说明了该方法的有效性。     

CARMA模型在液位控制系统中的应用

以比例阀的输出为系统输入,液位值为系统输出,对液位控制系统进行CARMA建模研究.选用AIC准则作为系统模型阶次的选择原则,以最小二乘法来辨识模型参数,辨识了系统的CARMA模型.模型的预测输出和实际输出的比较结果证实了CARMA建模在液位控制系统中的有效性.     

Robust identification for multi-section freeway traffic models

1IntroductionIt is important to estimate the densityandspeed oftrafficfor the safetyandtraffic control .For decades ,manyresearchwork have been done to estimate traffic density, trafficvolume ,average speed,and other parameters[1,2] .Theproblemof estimating dynamic traffic has been involved inparts of those research work[1 ~4] .By means of O_Dmatrix,some researchers have also made a series of studiesof traffic prediction and traffic layout estimation[5] .However , most of the research work m…     

Robust identification for multi_section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper.To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed.To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently.In the new model identification criterion,numerically efficient U-D factorization is used to avoid computing the determinant values of two complex matrices.By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed.Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.     

A new look at the statistical model identification

The history of the development of statistical hypothesis testing in time series analysis is reviewed briefly and it is pointed out that the hypothesis testing procedure is not adequately defined as the procedure for statistical model identification. The classical maximum likelihood estimation procedure is reviewed and a new estimate minimum information theoretical criterion (AIC) estimate (MAICE) which is designed for the purpose of statistical identification is introduced. When there are several competing models the MAICE is defined by the model and the maximum likelihood estimates of the parameters which give the minimum of AIC defined by AIC = (-2)log-(maximum likelihood) + 2(number of independently adjusted parameters within the model). MAICE provides a versatile procedure for statistical model identification which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure. The practical utility of MAICE in time series analysis is demonstrated with some numerical examples.     

Structure identification of generalized adaptive neuro-fuzzy inference systems

This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units. The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using the fuzzy curve. For structure identification, a new criterion called structure identification criterion (SIC) is proposed. SIC deals with a trade off between performance and computational complexity of the GANFIS model. The computational complexity of gradient descent learning is formulated based on simulation study. Three methods of initialization of GANFIS, viz., fuzzy curve, fuzzy C-means in x/spl times/y space and modified mountain clustering have been compared in terms of cluster validity measure, Akaike's information criterion (AIC) and the proposed SIC.     

The AIC Criterion and Symmetrizing the Kullback–Leibler Divergence

The Akaike information criterion (AIC) is a widely used tool for model selection. AIC is derived as an asymptotically unbiased estimator of a function used for ranking candidate models which is a variant of the Kullback-Leibler divergence between the true model and the approximating candidate model. Despite the Kullback-Leibler's computational and theoretical advantages, what can become inconvenient in model selection applications is their lack of symmetry. Simple examples can show that reversing the role of the arguments in the Kullback-Leibler divergence can yield substantially different results. In this paper, three new functions for ranking candidate models are proposed. These functions are constructed by symmetrizing the Kullback-Leibler divergence between the true model and the approximating candidate model. The operations used for symmetrizing are the average, geometric, and harmonic means. It is found that the original AIC criterion is an asymptotically unbiased estimator of these three different functions. Using one of these proposed ranking functions, an example of new bias correction to AIC is derived for univariate linear regression models. A simulation study based on polynomial regression is provided to compare the different proposed ranking functions with AIC and the new derived correction with AIC_c