获得线性模型中稳健初始估计的新方法*

本文首先给出一种从观测量中适当抽取若干样本获得线性模型中参数的稳健初始估计的新方法,其中心思想是寻求在一切线性估计中对辨识单个异常值性能优良的估计,该估计消除了杠杆点,对辨识任何位置上的异常值具有几乎同等的效率。在获得了稳健初始估计后,文中提出在递推估计过程中利用t-分布检验统计量逐个辨识线性模型中的所有异常值,最终求得线性模型中参数的极大似然估计。该方法适用于单站对空中飞行目标的一次跟踪数据处理,当数据可用一阶或二阶多项式线性模型描述时,其崩溃点δ~*=33％。该方法同时适用于低阶的稳健线性回归。     

基于模式识别的动态建模

本文提出了一种基于模式识别的辨识方法,以模式类的描述和模式分类来辨识系统结构,以基于模式识别的优化方法来估计系统参数,为在那些用传统方法建模困难的场合实现模型化提供了可能。     

基于遗传算法的模糊树建模方法

1　引言近年来模糊逻辑理论在非线性系统辨识领域中得到了广泛的应用 .模糊逻辑建模方法的主要内容可分为两个层次 ,一是模型结构的辨识 ,另一则是模型参数的估计 .模糊模型的结构辨识问题是指如何划分输入空间 .模糊树模型 (FT模型 )是一种利用二叉树结构描述输入空间模糊划分     

时域投影模型降阶方法的小样本误差估计

投影方法作为一类重要的模型降阶方法,其计算过程稳定,易于实现,但在理论上鲜有良好的时域误差估计结果．本文提出一种基于小样本估计过程的时域投影模型降阶误差估计方法．该方法首先将降阶过程中产生的误差分解为两部分,然后对各部分利用小样本估计方法进行估计．文中分别对线性和非线性输入输出系统进行小样本误差估计分析．此外,该方法能对线性系统的扰动问题进行分析,进一步的数值算例验证了该方法的有效性．     

非线性系统辨识

本文综述了非线性系统辨识问题，包括描述非线性系统的模型结构的辨识，模型参数的估计，并对可能的发展方向提出了作者的观点，最后介绍非线性系统辨识的若干应用。     

基于随机配置网络的非线性系统智能建模方法

针对一类非线性离散动态系统,研究非线性系统的智能建模方法.首先,采用带遗忘因子的递推最小二乘法对低阶模型的未知参数进行辨识;然后,对高阶非线性部分采用随机配置网络进行估计;最后,利用两种辨识方法在外部误差准则下对系统进行交替辨识,进而提出一种改进的非线性系统交替辨识的智能建模方法.将随机配置网络与递推最小二乘算法相结合,可有效提高非线性系统的辨识精度,并且通过数值仿真实验进行对比分析以验证所提出算法的有效性.     

一类双线性Hammerstein模型的集成辨识方法

对于一类静态非线性增益具有原点对称特性的双线性Ｈａｍｍｅｒｓｔｅｉｎ模型,提出了一种稳态与动态辨识相结合的集成辨识方法。该方法利用稳态信号获得稳态模型的强一致性估计,并通过稳态模型获得非线性增益的估计,再利用动态信息辨识获得双线性Ｈａｍｍｅｒｉｓｔｅｉｎ模型的双线性系统未知参数的一致性估计。仿真结果表明了该方法的有效性和实用性。     

一种基于UKF的系统参数在线辨识方法

本文介绍一种基于UKF的参数在线辨识方法。把用于状态估计的卡尔曼滤波算法通过转换用于系统模型的辨识上,最后使用该方法对直升机纵向模型进行在线辨识。仿真结果表明基于UKF的在线辨识方法是一种很好的在线辨识方法。     

一种模糊辨识方法及其在电站仿真器中的应用

利用模糊聚类和最小二乘估计方法提出一种糊辨识方法。该方法是基于模糊聚类,计算给定样本在各类中的隶属度,并利用递推最小二乘估计辨识模糊模型的后件参数。采用该方法对火力发电厂电站仿真器中的汽轮发电机密封油冷却系统进行建模研究,取得了满意的效果。     

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．     

单目人体图像序列的运动及结构参数估计

利用人体行走的单目动态图像序列,估计手臂及腿部的运动及结构参数.在物体中心坐标的运动表示方式下,提出了基于弹性连接概念的非刚体运动模型,通过加入弹性系数的方法,使非刚体运动模型和刚体运动模型得到了良好的统一.在此模型的基础上,用Levenberg-Marquardt方法实现了运动及结构参数的估计.实验证明了该方法的有效性,运动模型中的弹性系数也反映了一定程度的运动模式.     

基于模糊ARMAX模型的模糊建模

提出了一种利用MGS(modified Gram-Schmidt)算法建立模糊ARMAX模型的方法, 给出了基于MGS算法的模型结构和参数辨识的一体化方法. 利用MGS正交变换对通过GK模糊聚类的聚类结果进行变换, 确定对模型贡献大的规则, 删除对模型贡献小的规则, 同时对模型中的参数进行估计. 本文提出的方法能够实现模糊模型的结构和参数的优化. 仿真结果表明, 本文提出的方法能够建立非线性系统的模糊ARMAX模型.     

Missing Output Identification Model Based Recursive Least Squares Algorithm for a Distributed Parameter System

This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective.     

Blind system identification using precise and quantized observations

This paper studies the blind identification of multi-channel FIR systems using precise and quantized observations. First, a new deterministic blind identification (DBI) algorithm is presented for multi-channel FIR systems using precise observations, in which the system parameters can be consistently estimated and the common source signal can be stably recovered. When the observed samples are quantized by a static finite-level quantizer, an iterative deterministic blind identification (IDBI) method is then provided. The asymptotic characters of the proposed IDBI method are discussed and the quantization effect on the identification performance is analyzed. Numerical simulations are given to support the developed DBI method and IDBI method.     

A model predictive control approach with relevant identification in dynamic PLS framework

In this paper, a generalized predictive control (GPC) scheme under a dynamic partial least squares (PLS) framework is proposed. At the modeling stage, a model predictive control relevant identification (MRI) approach is used to improve the identification of the model. Within PLS framework, the MIMO system can be automatically decomposed into several SISO subsystems in the latent space. For each subsystem, MRI is implemented and GPC is designed independently. With the advantage of MRI and PLS framework, fewer parameters are needed to be estimated in the identification stage, nonsquare and ill-conditioned system can be handled naturally, control parameters tuning is easier and better control performance can be obtained. Furthermore, the computing time of control action which is very crucial for GPC on-line application decreases since each GPC is running in SISO subsystem in parallel. The results of two simulation examples and a laboratory experiment demonstrate the merit of the proposed method.     

基于脉冲响应的输出误差模型的辨识

基于系统脉冲响应参数, 利用相关分析方法, 提出了一种辨识输出误差模型参数的方法. 该方法是利用有限脉冲响应模型逼近输出误差模型, 通过依次递增脉冲响应参数的数目N来提高逼近精度. 理论分析表明, 只要N足够大, 模型的辨识精度可以满足实际要求. 提出的辨识方法可以在假设阶次N =1的条件下, 依次递增计算N较大时的脉冲响应参数和目标函数值, 从而根据脉冲响应确定系统的参数. 仿真试验说明提出的方法估计输出误差模型的参数是有效的.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

A two‐stage searching method for continuous time‐delay systems identification

This paper presents a novel method to estimate the unknown parameters of continuous‐time systems with time delay. In the proposed method, the time delay and plant parameters are estimated separately. To estimate the time delay, a one‐dimensional searching method with variable step size is proposed to improve computational efficiency. The searching method consists of two stages: the coarse stage and the refined searching stage. To analyze the convergence of the searching method, the concept of significant interval is proposed. By defining the significant interval, a sufficient condition for global convergence of the searching method is provided. Based on the two‐stage searching method, a novel identification algorithm is developed in which the simplified refined instrumental variable for continuous‐time models algorithm is used to estimate the plant parameters. Simulation results demonstrate that the proposed identification method can estimate the unknown parameters of continuous‐time system with time delay efficiently. The estimation results under different noisy conditions verify the reliability and robustness of the proposed method. The applicability of the developed identification method is demonstrated by a practical example.