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.     

基于组合式信号源的Hammerstein-Wiener模型辨识方法

针对含有有色噪声的非线性Hammerstein-Wiener模型,提出一种基于组合式信号源的辨识方法.通过利用可分离信号和随机信号组成的组合信号源实现有色噪声干扰下Hammerstein-Wiener模型各串联模块参数辨识的分离,简化辨识过程.首先,基于可分离信号的输入和输出,采用相关分析方法抑制过程噪声的干扰,辨识输出静态非线性模块和动态线性模块的参数;然后,基于辅助模型技术,利用辅助模型的输出和残差的估计值分别取代辨识模型中的不可测中间变量和噪声变量,推导辅助模型递推增广最小二乘方法,根据随机信号的输入输出数据辨识输入静态非线性模块和噪声模型的参数;最后,通过理论分析和仿真结果表明,所提出方法能够有效辨识有色噪声干扰下的非线性Hammerstein-Wiener模型,具有较好的鲁棒性.     

条件随机场与多层算法模型的实体自动识别

实体自动识别技术是人们获取信息的有力手段,也是自然语言处理研究的关键技术之一。目前命名实体识别的研究较多,且已趋于成熟,而对汉语文本中的其他实体（名词性、代词性）研究较少。因此提出了一体化识别命名实体识别和名词性实体的方法,该方法将实体的汉字、分词、词性标注等信息引入条件随机场;再利用多层算法模型优化已经识别出的实体,以及召回未识别出的实体。在标准ACE语料库上进行实验,正确率达到75.56%,召回率达到72.52%。结果表明该方法对于实体识别问题是有效的。     

基于双重特征的协议识别方法

随着Internet技术的发展,网络应用服务越来越丰富多彩。为了对目前互联网中的流量实施有效监控,需要使用协议识别技术,因此,协议识别方法已成为研究热点。然而随着网络协议的复杂化,一些传统的协议识别方法已经不能够准确地识别协议。主要介绍基于数据包Payload特征的识别方法与基于流量特征的SVM识别方法,并提出一种将两者结合的协议识别方案。     

Multi-frequency identification method in signal processing

Virtually vibration signals are always composed of many frequency components. Using the least squares method, a new multi-frequency identification algorithm has been proposed to identify the amplitude and phase of each component of a multi-frequency signal, and it will work well as long as the main frequency values are known. Based on this algorithm, two similar optimization procedures have been presented to obtain accurate frequency values for a sine-wave signal and a bi-tone signal, respectively. Assisted with band-pass filters and these optimization procedures, the multi-frequency identification algorithm can successfully identify the multi-frequency components of a signal. The multi-frequency identification method can be used to identify oil-film coefficients of journal bearing. And some reliable oil-film coefficients have been reached.     

基于轮廓特征点的高精度识别目标方法

为了实现高成功率、高精度和快速识别跟踪目标,提出对基于轮廓特征点的目标精确识别方法。在识别过程中采用了轮廓提取和多边形拟合算法自动搜寻到图像中要识别和跟踪的目标,同时对目标物轮廓的多边形角点进行亚像素分辨率的定位,从而可以利用目标轮廓角点的精确定位来实现对多边形目标的识别与跟踪。试验结果以及特征点亚像素算法分辨率的分析表明,采用这种自动识别与跟踪目标的方法,其精度可以达到0．02像素。     

空间谱估计和数字波束形成的RFID信号辨识

针对超高频和微波段射频识别系统(RFID)信号在噪声干扰和多标签环境下难以辨识的问题,提出一种新的RFID信号辨识方法。以均匀直线阵为基础,采用空间谱估计算法正确估计出波达方向(DOA),再根据信号波达方向通过数字波束形成技术产生自适应定向波束来辨识信号。仿真分析表明,该方法具有较低的信噪比门限和较高的信号辨识和防碰撞性能。     

Web日志挖掘中的会话识别方法

为更好地实现会话识别,从而为后续模式发现提供准确的挖掘数据,在分析现有常用的会话识别方法后,提出一种基于待挖掘站点首页的用户会话识别方法.该方法根据用户浏览站点的习惯,以站点首页作为用户新会话开始标识,并增强了用户会话的定义.最后利用数据库编程实现该方法,将识别出的会话存储在数据库中,便于后续数据挖掘使用.实验结果表明,该方法能识别出更多的用户会话,且识别会话的准确率也更高.     

Identification of continuous-time Hammerstein systems by simultaneous perturbation stochastic approximation

This paper proposes an identification method for Hammerstein systems using simultaneous perturbation stochastic approximation (SPSA). Here, the structure of nonlinear subsystem is assumed to be unknown, while the structure of linear subsystem, such as the system order, is assumed to be available. The main advantage of the SPSA-based method is that it can be applied to identification of Hammerstein systems with less restrictive assumptions. In order to clarify this point, piecewise affine functions with a large number of parameters are adopted to approximate the unknown nonlinear subsystems. Furthermore, the linear subsystems are supposed to be described in continuous-time. Though this class of systems closely reflects the actual systems, there are few methods to identify such models. Hence, the SPSA-based method is utilized to identify the parameters in both linear and nonlinear subsystems simultaneously. The effectiveness of the proposed method is evaluated through several numerical examples. The results demonstrate that the proposed algorithm is useful to obtain accurate models, even for high-dimensional parameter identification.     

基于ANFIS的非线性系统辨识研究

系统辨识是控制系统设计的基础，对非线性系统进行辨识是当前的难点；文献[1]提出了用模糊建模方法，文献[2]提出了用神经网络方法，在总结上述方法不足的基础上，该文提出了用自适应神经模糊推理系统(ANFIS)对非线性系统进行辨识的方法，仿真结果表明，ANFIS进行非线性系统辨识是可行的，其辨识精度很高。     

Identification of both structural damages in bridge deck and vehicular parameters using measured dynamic responses

A method to identify both damages in bridge and the vehicular parameters from the structural dynamic responses is presented. A dynamic response sensitivity-based finite element model updating approach is used to identify both the structural damages and the vehicular parameters. The solution is obtained iteratively with the penalty function method with regularization from the measured dynamic responses. The effects of measurement noise, different vehicle models, measurement time duration and modeling error on the identification results are investigated in the numerical simulation. Studies in this paper indicate that the proposed method is efficient and robust for both damage and parameter identification.     

基于流量与行为特征的P2P流量识别模型

针对点对点(P2P)用户习惯、运行环境的异构性,提出P2P流量识别的双层模型。该模型由单流内部流量特征的贝叶斯网络识别算法与多流之间行为特征的支持向量机识别算法组成。实验结果表明,相对于统计特征识别方法,该模型检测准确度提高5.4%,且对于不同应用场景具有较好的稳定性。     

基于历史数据的高速多义路径概率识别方法

高速公路多义路径问题是指如何在具有多条可选路径的高速公路网中确定车辆的一条驶经路径.目前普遍采用的基于识别点的多义路径识别方法在某些情况下（如设备故障、环境亮度或透明度不够等）存在识别率低的问题,导致一些时段存在车辆多义路径难以识别.针对以上情况,本文提出一种基于历史数据的多义路径概率识别方法,通过基于路段的聚类方法计算各路段概率值,然后结合贪心算法找出车辆的驶经路径,用来在识别设备故障时辅助识别多义路径.该方法可以有效的在识别设备故障时识别多义路径,提高了该方法的准确度.     

随机子空间方法在齿轮系统故障诊断中的应用

在齿轮系统故障诊断中.采用何种有效的方法对随机动态信号进行分析和特征提取是关键所在;相对于普通的分析方法而言结构模态参数辨识领域先进的时域识别方法随机子空间能更准确地识别出环境激励下结构系统的模态参数;将这一方法引入到运转的齿轮系统中进行了动态特性识别,对齿轮系统发生的故障进行特征提取、区分与诊断,并与正常齿轮系统对比,实验结果表明,该方法可以有效地对齿轮传动系统故障进行识别与诊断。     

基于笔迹的身份鉴别

提出了一种鉴别笔迹的新方法.现有的笔迹识别方法大多需要进行分割或关联部分 的分析,都是与内容相关的方法.在新方法里,把手写笔迹作为一种纹理来看待,将笔迹鉴别 的问题转化为纹理识别来处理,这是一种与内容无关的方法.使用多通道二维Gabor滤波器 来提取这些纹理的特征,并使用加权欧氏距离分类器来完成匹配工作.在实验中,使用了17个 人的不同笔迹,取得了很好的结果.     

基于k近邻变量贡献与重构理论的工业过程故障诊断

针对工业过程中发生故障时异常变量的精确识别以及如何准确建立"故障–征兆"表的问题,本文提出了一种基于k近邻(k–NN)变量贡献分析和数据重构的异常变量精确识别方法.首先,将k–NN算法中各个采样时刻的统计距离指标细化,分解为每个变量的贡献并对其进行详细分析,分别从单变量和多变量异常角度进行方法的可行性验证,确定过程故障时异常变量具有较大的贡献值;其次,建立正常数据中每个变量的贡献模型用于对故障样本中的异常变量进行"一次"识别;随后提出基于k–NN理论的数据重构算法,并从重构原理方面进行分析,验证该方法具有一定的有效性.对于故障样本,根据变量贡献分析方法求取每个变量对距离指标的贡献,"一次"识别出故障发生时所对应的异常变量或征兆;进而通过数据重构理论对故障样本中异常变量值进行重构、检测和"二次"识别,直至辨识出过程中发生异常的所有变量,并得到故障与变量之间的关系,即"故障–征兆"表.     

Correlation analysis method based SISO neuro-fuzzy Wiener model

A novel identification algorithm for neuro-fuzzy based single-input-single-output (SISO) Wiener model with colored noises is presented in this paper. The separable signal is adopted to identify the Wiener model, leading to the identification problem of the linear part separated from nonlinear counterpart. Then, the correlation analysis method can be employed for identification of linear part. Moreover, in the presence of random signal, the least square method based parameters estimation algorithm of static nonlinear part are proposed to avoid the impact of colored noise. As a result, proposed method can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of Wiener model. Examples are used to verify the effectiveness of the proposed method.     

基于电压递推与FFRELS的航空蓄电池模型参数动态辨识

摘要：针对电池等效电路模型参数具有时变性,难以准确辨识的问题,建立了二阶RC航空电池模型,提出了基于遗忘因子扩展递推最小二乘法(FFRELS)的参数辨识算法;同时针对电池开路电压测量时间长的问题,创建了蓄电池开路电压辨识递推模型,实现开路电压的动态估计,该方法能够准确辨识出模型的参数,有效提高蓄电池开路电压的辨识精度。最后通过仿真实验,验证了该方法能够提高电池模型参数的辨识精度。     

Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises

A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of nonlinear part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear part and static nonlinear part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.     

Comment on ‘Frequency domain identification of Wiener models’, by E.W. Bai, Automatica 39 (2003), 1521–1530

A frequency-domain identification method is proposed by Bai (2003) for Wiener systems. The key component is a phase estimator that gives estimates of the linear subsystem phase for different frequencies. These estimates are then used to identify the whole system. In this note, it is shown that the phase estimator is not generally consistent. Consequently, consistency of the overall identification method is in turn not generally guaranteed.