Magnitude bounds of generalized frequency response functions for nonlinear Volterra systems described by NARX model

In order to reveal the relationship between system time domain model parameters and system frequency response functions, new magnitude bounds of frequency response functions for nonlinear Volterra systems described by NARX model are established. The magnitude bound of the nth-order generalized frequency response function (GFRF) can be expressed as a simple n-degree polynomial function of the magnitude of the first order GFRF, whose coefficients are functions of the model parameters and frequency variables. Thus the system output spectrum can also be bounded by a polynomial function of the magnitude of the first order GFRF. These results demonstrate explicitly the analytical relationship between model parameters and system frequency response functions, and provide a significant insight into the magnitude based analysis and synthesis of nonlinear systems in the frequency domain.     

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.     

Numeric and curve parameters for freeform surface feature models

The success of feature modelling systems depends to a large extent on offering user-friendly ways for specifying and varying feature models. In this paper, several new facilities are introduced for this.Numeric and curve parameters are presented for specification and variation of freeform surface feature models. Several properties of the parameters can be defined, such as the direction of evaluation for a curve parameter. In addition to parameters present in features, other parameters can be added to a feature model, including intersection curves between features.To provide these facilities, a new three-level structure is introduced, which includes a partially evaluated model that can capture new generic model entities, including feature intersection curves. The work has been implemented in a prototype surface feature modelling system with functionality for feature class definition, feature model creation, and advanced feature model variation through the new types of parameters.     

基于曲率流的点模型编辑方法*

为了对点模型表面进行保细节的自由形状编辑,提出了一种基于曲率流的点模型形状编辑方法。该方法定义了基于平均曲率流的光顺算子,通过此光顺算子获得点模型不同光滑程度的曲面表示。运用对应曲面上点之间的向量差进行相应几何细节的抽取,在相邻的曲面表示之间将几何细节剥离出来。用户可以在不同的曲面上进行自由形状编辑,而无须关心几何细节。在变形后的曲面上再将相应的几何细节映射回去,实现对模型保细节的变形。实验结果表明该方法是一种有效的点模型造型算法,在变形的同时,有效地保持了模型表面的细节特征。     

基于OWL的本体集成

提出一种新的本体集成方法。分析了本体集成的原因,阐述了本体集成时应遵循的4条基本原则,并给出了集成的分类,提出了一种基于OWL DL图闭包的本体集成方法。该方法将OWL DL本体抽象为RDFS图模型,根据给定的OWL DL推理规则生成OWL DL本体的图闭包,在此基础上进行本体集成,同时提出了几种计算实体相似度的方法,将本方法与COMA++和FCA-merge进行实验对比,本方法在准确率和召回率上占优势。     

基于动态贝叶斯网络的社交圈归属匹配模型

为了帮助社交网中新成员寻找与之最为合适的社交圈,尝试采用动态贝叶斯网（DBN）理论解决社交网分析应用中成员（节点）与社交圈（集合）的匹配问题。将圈内成员个人的多项兴趣爱好程度作为描述社交圈基本属性的特征向量,对每一类圈子建立了带有辅助信息形式的DBN模型,求解最大输出概率即为最佳匹配对象。结果表明,在客观测试和主观评价两方面,该模型都收到了较为满意的结果。     

基于组合算法的中文反垃圾邮件分类系统的研究*

论述了一种采用组合算法实现的垃圾邮件分类系统,并在Windows平台下用Visual Basic 6.0实现。本系统工作在邮件客户端,基于邮件内容的解析,相对于只使用基于分类器的垃圾邮件分类系统,不仅能有效快速地分类邮件,同时提高了分类的精度、降低误判率。     

基于ActiveX控件的InTouch控制功能扩展

针对目前组态软件InTouch控制功能不够完善,需要用户进行二次开发的情况,提出一种组态软件扩展控制功能的方法。用Matlab COM生成器生成先进控制算法COM组件,在Visual Basic中开发调用算法组件的控制器ActiveX控件,并将控件引入到组态软件InTouch中,为其扩展单神经元自适应PID控制器功能。仿真实验证明了该方法的可行性。     

支持遗忘特征的记忆模型及其在知识管理中的应用

为了在知识管理系统中有效进行知识筛选,本文将遗忘的特性引入知识管理,建立了复合记忆—遗忘 数学模型．探讨了该模型的数学特性,并对其进行了适合计算机模拟的等效简化．探讨了该模型在知识管理系统中 的应用和实现方法,并给出了实例．该模型有助于识别知识管理系统中的老化知识和新知识,优选关键知识,提高 系统效率．     

一种改进的基于向量的P2P电子商务信任模型

在交易之前通过信任模型帮助交易对象之间建立信任关系,是P2P电子商务发展的必要条件,也是近年来的研究热点。本文针对现有模型的不足,在基于投票协议的P2P电子商务安全信任模型的基础上,引入了向量表示信任,并且在权限的确定、信任的表述和信任的评估等多方面做了改进。经过模拟实验和分析证明,改进后的模型不仅可以让用户根据各自的不同要求对交易对象做出更合理的信任评估,而且能快速识别和隔离恶意节点,从而降低了交易风险。