The refinement algorithm consideration in text clustering scheme based on multilevel graph

To construct a high efficient text clustering algorithm, the multilevel graph model and the refinement algorithm used in the uncoarsening phase is discussed, The model is applied to text clustering. The performance of clustering algorithm has to be improved with the refinement algorithm application, The experiment result demonstrated that the muhilevel graph text clustering algorithm is available,     

两种数字生命的Swarm仿真研究

数字生命的概念是托马斯·雷最早在计算机上模拟实验提出的,以此为指导,介绍了复杂适应系统、Agent建模等理论知识,利用Swarm仿真平台构建了两种异类数字生命及共同生存的环境,模拟自然界生命体最基础的繁殖歼灭等生命现象,通过可视化窗口观察生命体个体及群体间的交互行为,得到结果的不可测性和突现性,实现了两种数字生命的Swarm仿真。     

变权值LMF/LMS自适应多用户检测器

提出了一种改进型的LMF/LMS自适应多用户检测器,即变权值互补比较LMF/LMS多用户检测器(VWCCLMF/LMS-MUD)。该检测器通过两个滤波器分别控制检测器的收敛速度和稳态性,并在最小均方误差的准则下调整权值比例。计算机仿真结果表明,该检测器在高斯和瑞利信道下均具有比LMS算法更快的收敛速度和比LMF算法更小的稳态误差。     

一种基于Chirplet分解的时频分析快速算法

针对信号自适应chirplet分解未知参数多、实现起来比较困难的特点,提出了一种新的chirplet分解快速算法．该算法利用计算信号的二次相位函数,得到其能量分布集中于信号的调频率曲线上的结论,此时通过谱峰检测可同时获得chirplet调频率、时间中心和幅度的估计;然后通过解线性调频技术获得其初始频率和宽度的估计．仿真结果验证了本文算法的有效性．     

Al—Ti—CN细化工业纯铝工艺参数测定

继Al—Ti—C中间合金之后,又开发了Al—Ti—CN中间合金,通过大量细化工业纯铝试验,从中间合金加入量、加热温度、保温时间等工艺试验,找出Al—Ti—CN中间合金细化工业纯铝的最佳工艺.对Al—Ti—CN用于工业生产提供有益参数.     

氰乙酸乙酯脱酸精制固体碱吸附剂的工业放大试验研究

采用混捏法制备出MgO/γ-Al2O3固体碱吸附剂,测定了固体碱的脱酸效率、酸容量、吸附热及抗流失性能.通过固定床考察了固体碱吸附剂对氰乙酸乙酯样品的吸附脱酸效果,并对固体碱吸附剂的工业放大性能进行了考察.试验结果表明,当原料氰乙酸乙酯酸值为58×10 -6、控制吸附脱酸后的酸值小于10×10-6时,实验室制备样品的脱酸能力达到240∶1(体积比);工业放大制备的MgO/γ-Al2O3固体碱,当进料酸值为60×10 -6 ～80×10 -6,精制后酸值不超过20×10 -6时,固体碱吸附剂脱酸能力为173∶1(质量比).氰乙酸乙酯润湿固体碱过程平均热效应为61.91 J ·g-2(催化剂);所制备固体碱吸附剂活性组分具有良好的抗冲刷性能,吸附精制后残留在氰乙酸乙酯样品中的金属镁质量分数低于2.0×10 -6.     

Ck-Banach(k≥1)流形之间非线性映射的局部线性化

在参考文献[1]中,给出了Banach空间之间非线性映射的局部线性化定理,本文将上述结果进行推广,考虑Ck-Banach流形之间非线性映射局部线性化问题.     

势函数自适应加权模糊C-均值聚类方法

提出了一种新的基于势函数自适应加权的模糊 C-均值 ( PAWFCM)聚类算法。该方法以样本的空间势几何结构为基础 ,自适应计算出样本的加权矩阵 ,然后利用加权模糊 C-均值算法对样本集合进行分类。由于自适应地考虑到了不同样本点对分类的影响程度 ,对较复杂的样本集合 ,能明显提高分类的正确性和鲁棒性。算法的几个典型实验也证明了这一点     

Recursive estimation and forecasting of non-stationary time series

The paper presents a unified, fully recursive approach to the modelling and forecasting of non-stationary time-series. The basic time-series model, which is based on the well-known ‘component’ or ‘structuraL’ form, is formulated in state-space terms. A novel spectral decomposition procedure, based on the exploitation of recursive smoothing algorithms, is then utilized to simplify the procedures of model identification and estimation. Finally, the fully recursive formulation allows for conventional or self-adaptive implementation of state-space forecasting and seasonal adjustment. Although the paper is restricted to the consideration of univariate time series, the basic approach can be extended to handle explanatory variables or full multivariable (vector) series.     

AN EXPONENTIAL INEQUALITY FOR AUTOREGRESSIVE PROCESSES IN ADAPTIVE TRACKING

A wide range of literature concerning classical asymptotic properties for linear models with adaptive control is available, such as strong laws of large numbers or central limit theorems. Unfortunately, in contrast with the situation without control, it appears to be impossible to find sharp asymptotic or nonasymptotic properties such as large deviation principles or exponential inequalities. Our purpose is to provide a first step towards that direction by proving a very simple exponential inequality for the standard least squares estimator of the unknown parameter of Gaussian autoregressive process in adaptive tracking.