有界误差模型的一种结构辨识方法1)

针对具有未知但有界(UBB)误差的线性回归模型辨识问题,提出了一种新的鲁棒结构选择方法.该方法以重复递推椭球外界算法所得椭球轴信息阵的行列式相对值最大作为模型定阶准则.不同于以往对噪声独立性、常方差或鞅差特性的假设,该方法假设噪声是渐近独立的.文中证明了该方法的强相容性.     

自适应噪声定界的改进集员辨识算法

集员辨识所需的系统噪声边界在现实应用中往往难于精确确定, 通常采取的过估边界将导致算法性能的退化. 本文针对缺乏足够先验噪声边界知识下的集员辨识问题进行了相应的研究, 通过对输入干扰和测量误差的有界假设, 将系统噪声边界建模为一个依赖于模型参数的时变量, 由此提出了一种根据估计参数自适应调定噪声边界的改进最优定界椭球辨识算法, 避免了过估噪声边界假设引起的保守性增大的缺陷, 提高了算法的收敛速度. 仿真中将本文提出的改进算法和带固定过估噪声边界的原始算法进行了比较, 表明了该方法的有效性.     

一种新的基于保证定界椭球算法的非线性集员滤波器

基于未知但有界噪声假设的集员滤波器为传统的概率化滤波方法提供了一种可行的替代选择, 然而其潜在的计算负担和保守性考虑制约了该方法的实际应用. 本文提出一种新的基于保证定界椭球近似的改进集员滤波方法, 用于解决针对非线性系统的状态估计问题,在保证实时性的前提下降低了算法的保守性. 首先,对非线性模型进行线性化处理,采用DC (Difference of convex)规划方法对线性化误差进行外包定界, 并通过椭球近似将其融合到系统噪声中; 在此基础上提出了一种结合了椭球直和计算和基于迭代外定界椭球算法的椭球--带交集计算 所构成的经典预测--更新步骤来估计得到状态的可行椭球集. 与常规的非线性扩展集员滤波方法的仿真比较表明了本文所提出算法的有效性和改进性能.     

GPS软件接收机载波跟踪环路鲁棒滤波算法

在全球定位系统(GPS)软件接收机中,环路滤波器对噪声抖动的抑制作用,是用户精确连续跟踪卫星信号的重要保证.本文用噪声未知但有界的假设,代替了传统方法中噪声统计特性已知的苛刻要求,应用半定规划方法将滤波问题转化为凸优化问题,提出了载波跟踪环路鲁棒滤波算法,获得了包含多普勒频移的置信椭球,解决了复杂多变环境下GPS软件接收机的滤波跟踪问题.应用模拟载波信号和实际卫星信号对该算法进行验证,结果表明该方法能够连续有效跟踪GPS卫星信号,为GPS软件接收机设计环路滤波器提供了新的思路.     

基于MIT规则的自适应扩展集员估计方法

用于非线性椭球估计的自适应扩展集员(Adaptive extended set-membership filter, AESMF)算法在实际应用中存在着过程噪声设定椭球与真实噪声椭球失配的问题, 导致滤波器的估计出现偏差甚至发散. 本文提出了一种基于MIT规则过程噪声椭球最优化的自适应扩展集员估计算法(MIT-AESMF), 用于解决非线性系统时变状态和参数的联合估计和定界中过程噪声无法精确建模问题的新算法. 本算法通过MIT优化规则,在线计算使一步预测偏差包络椭球最小化的过程噪声包络椭球, 以此保证滤波器健康指标满足有效条件; 最后, 采用地面移动机器人状态和动力学参数联合估计验证了所提出方法的有效性.     

多输入多输出系统参数的集员辨识

本文讨论了多输入多输出系统参数的集员辨识。在系统动态噪声功率有界的假定下导出了ＭＩＭＯ系统参数的成员集合是个椭球，证明在一定条件下椭球趋向于一点。文中的仿真例子验证了上述结果。     

集员辨识理论发展及算法综述

基于UBB噪声假设下的集员辨识是一种新型的系统辨识方法,与传统辨识方法相比具有较强的鲁棒性.本文以参数线性模型为例介绍了集员辨识理论的发展历程和相关算法.     

Hammerstein系统参数的集员辨识

本文运用多输入多输出系统参数的集员辨识的结果讨论了Ｈａｍｍｅｒｓｔｅｉｎ系统参数的集员辨识．在系统动态噪声有界的假定下导出了Ｈａｍｍｅｒｓｔｅｉｎ系统参数的成员集是个椭球，并证明了在一定条件下椭球趋向于一点，文中还给出了仿真例子．     

基于最优定界椭球的扩展集员滤波算法研究

针对噪声未知但有界的非线性系统,提出了一种基于最优定界椭球的扩展集员滤波算法.首先,对非线性状态方程和量测方程进行泰勒级数展开,采用区间分析方法对线性化误差进行外包定界,并通过椭球近似将其整合到系统噪声中;在此基础上通过预测步和滤波步两步过程来计算与量测和噪声边界近似相一致的待估计状态的可行集.以Duffing方程为例,与扩展卡尔曼滤波方法的仿真比较表明了该算法的精确性和鲁棒性.     

实现闭环系统参数一致估计的直接辨识方法

为了实现闭环系统参数的一致估计，提出了一种一致辨识闭环系统模型参数的偏差补偿最小二乘法，这种方法不需要事先假设系统模型阶次和通道时延满足一定的不等式，允许前向通道的反馈通道一中的噪声为有色噪声，并且在其辨识过程中不需要对有色噪声建模。     

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequently, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-sprit stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass fdter to elinfinate the off-band noise, and then performs time-shared bfind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.     

一类工业过程的PID控制器整定方法

PID调节器具有结构简单、适应性强、不依赖于被控对象的精确模型、鲁棒性较强等优点。控制器的性能直接关系到生产过程的平稳高效运行以及产品的最终质量。针对一类可用带有纯时间延迟的一阶对象来近似描述的工业过程,本文通过对工业过程现场采集的数据,利用Levenberg-Marquardt最小二乘拟合法辨识出被控对象的数学模型,进而根据典型PID控制器的参数整定方法求出PID控制器的参数。通过对一个典型工业对象的仿真研究,结果表明所提出的方法简单有效。     

Analysis and identification of speed-independent circuits on an event model

The object of this article is the analysis of asynchronous circuits for speed independence or delay insensitivity. The circuits are specified as a netlist of logic functions describing the components. The analysis is based on a derivation of an event specification of the circuit behavior in a form of a signal graph. Signal graphs can be viewed either as a formalization of timing diagrams, or as a signal interpreted version of marked graphs (a subclass of Petri nets). The main advantage of this method is that a state explosion is avoided. A restoration of an event specification of a circuit also helps to solve the behavior identification problem, i.e., to compare the obtained specification with the desired specification. We illustrate the method by means of some examples.     

基于图像处理的蔗糖结晶颗粒识别方法

针对煮糖过程蔗糖结晶图像的特点采用颜色空间转换的方法,将图像从RGB颜色空间转换为HIS颜色空间,利用HIS颜色空间各分量相对独立性以及结晶颗粒和糖浆溶液的色调差异通过改进的大津法对H分量进行阈值分割,再通过数学形态学,中值滤波,孔洞填充,去除噪声颗粒等方法进行后续处理,最后对处理后的图像提取结晶颗粒的个数、面积、周长、形状指数以及面积比等特征值并进行分析来完成煮糖过程中蔗糖结晶颗粒的识别。实验证明该方法识别率高,切实可行。     

一种基于高阶累积量的因果结构学习算法

从观测数据中学习因果结构具有重要的应用价值。目前,一类学习因果结构的方法是基于函数因果模型假设,通过检验噪声与原因变量的独立性来学习因果结构。然而,该类方法涉及高计算复杂度的独立性检验过程,影响结构学习算法的实用性和鲁棒性。为此,提出了一种在线性非高斯模型下,利用高阶累积量作为独立性评估的因果结构学习算法。该算法主要分为两个步骤,第一个步骤是利用基于条件独立性约束的方法学习到因果结构的马尔可夫等价类,第二个步骤是定义了一种基于高阶累积量的得分,该得分可以判别两个随机变量的独立性,从而可以从马尔可夫等价类中搜索到最佳独立性得分的因果结构作为算法的输出。该算法的优势在于：a)相比基于核方法的独立性检验,该方法有较低的计算复杂度;b)基于得分搜索的方法,可以得到一个最匹配数据生成过程的模型,提高学习方法的鲁棒性。实验结果表明,基于高阶累积量的因果结构学习方法在合成数据中F₁得分提高了5%,并在真实数据中学习到更多的因果方向。     

Virus coevolution partheno-genetic algorithms for optimal sensor placement

A virus coevolutionary partheno-genetic algorithm (VEPGA), which combined a partheno-genetic algorithm (PGA) with virus evolutionary theory, is proposed to place sensors optimally on a large space structure for the purpose of modal identification. The VEPGA is composed of a host population of candidate solutions and a virus population of substrings of host individuals. The traditional crossover and mutation operators in genetic algorithm are repealed and their functions are implemented by particular partheno-genetic operators which are suitable to combinatorial optimization problems. Three different optimal sensor placement performance index, one aim on the maximization of linear independence, one aim on the maximization of modal energy and the last is a combination of the front two indices, have been investigated. The algorithm is applied to two examples: sensor placement for a portal frame and a concrete arc dam. Results show that the proposed VEPGA outperforms the sequential reduction procedure (SRP) and PGA. The combined performance index makes an excellent compromise between the linear independence aimed index and the modal energy aimed index.     

快速独立分量分析法识别结构模态参数研究

采用快速独立分量分析(FastICA)算法,快速、准确地识别结构模态参数.该算法以模态响应之间的独立性为依据构建出目标函数,并以此目标函数为基准采用ICA算法对结构输出信号进行分离,而得到结构振型向量.进而通过单模态识别技术-希尔伯特(Hilbert)变换,识别出结构的频率和阻尼比.最后通过白噪声激励下六层框架结构的模态参数识别,验证了快速独立分量分析算法识别结构模态参数的可行性和鲁棒性.     

非最小相位线性系统的可辨识性及辨识方法——平稳输入

本文首先将传统意义下的不相关及其与协方差函数之间的关系推广到n(n≥2)阶不相关及其与高阶累积函数之间的关系。以此为基础,讨论了非最小相位线性随机系统的可辨识性及辨识方法。将可辨识性条件从独立同分布放宽到n(n>2)阶不相关。对用熵函数进行辨识的方法做了较详细的研究并给出了可辨识性定理。     

Model-based and actual independence for fairness-aware classification

The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The main purpose of this paper is to analyze this formal fairness in order to achieve better trade-offs between fairness and prediction accuracy, which is important for applying fairness-aware classifiers in practical use. We focus on a fairness-aware classifier, Calders and Verwer’s two-naive-Bayes (CV2NB) method, which has been shown to be superior to other classifiers in terms of fairness. We hypothesize that this superiority is due to the difference in types of independence. That is, because CV2NB achieves actual independence, rather than satisfying model-based independence like the other classifiers, it can account for model bias and a deterministic decision rule. We empirically validate this hypothesis by modifying two fairness-aware classifiers, a prejudice remover method and a reject option-based classification (ROC) method, so as to satisfy actual independence. The fairness of these two modified methods was drastically improved, showing the importance of maintaining actual independence, rather than model-based independence. We additionally extend an approach adopted in the ROC method so as to make it applicable to classifiers other than those with generative models, such as SVMs.