一种基于无网格方式的弹塑性体变形仿真方法

提出了一种对高弹性、高塑性物体变形进行仿真的新方法。该算法利用点采样的方式表现表面,使用采样分裂和聚合以适应仿真中采样密度的变化。将弹性力学中的理论应用到仿真中,使用移动最小二乘法计算应力、张力、弹性体力,使用张力状态变量来描述弹塑性变化,利用向量位移进行了仿真。仿真结果可表现融化、分裂、聚合等弹塑性变形。     

Adaptive control of system with hysteresis using neural networks

1.INTRODUCTIONThe piezoelectric actuators are well suited for micro-position devices in precision engineering because oftheir fast response,nanometer resolution and biggerdriving force[1].However,hysteresis inherent topiezoelectric actuator severely li mits system’s perfor-mance such as giving rise to undesirable accuracy oroscillations,even leading to instability.Hysteresischaracteristics are generally nondifferentiable andusually unknown.It is a difficult task to mitigate itsharmful ef…     

Multicast routing algorithm of multiple QoS based on widest-bandwidth

1 .INTRODUCTIONA mobile Ad hoc network refers to a temporal multi-hop autonomy system,whichis constituted by a groupof mobile nodes containing wireless send-receive equip-ments[1].It possesses manycharacteristics-fast-deploy-able,self-organized,acentric , movable and of multi-hop.It can be wildly applied to many fields ,such asnational defense,emergency and disaster ,scientificin-vestigation and exploration etc .,and uponthat ,it hasgreat prospects[1 ,3 6]. With the wide spread of mo-bile…     

Research on Chaotic Time Series Prediction Based on K-entropy and RBF Neural Networks

In this paper, a method of direct multi-step prediction of chaotic time series is proposed, which is based on Kolmogorov entropy and radial basis functions neural networks. This is done first by reconstructing a phase space using chaotic time series, then using K-entropy as a quantitative parameter to obtain the maximum predictability time of chaotic time series, finally the predicted chaotic time series data can be acquired by using RBFNN. The application considered is Lorenz system. Simulation results for direct multi-step prediction method are compared with recurrence multi-step prediction method. The results indicate that the direct multi-step prediction is more accurate and rapid than the recurrence multi-step prediction within the maximum predictability time of chaotic time series. So, it is convenient to forecast and control with real time using the method of direct multi-step prediction.     

The glass transition temperature and microstructure of polyurethane/epoxy resin interpenetrating polymer networks nanocomposites

GLASS TRANSITION TEMPERATURE (TG) HAS BEEN EXTEN- SIVELY STUDIED FOR MANY POLYMERS SINCE IT IS ONE OF THE MOST FUNDAMENTAL FEATURES RELATIVE DIRECTLY TO POLYMER PROPERTIES. IN GENERAL, INORGANIC NANOFILLERS DO NOT DI- RECTLY CONTRIBUTE TO THE TG OF THE MA…     

一族奇异摄动时滞方程的有限差分法

考虑一族奇异掇动时滞微分方程．基于奇异摄动时滞方程准确解的性质，在分片等矩的Shishkin型网格上构造了线性奇异摄动时滞方程的有限差分格式，证得数值结果是关于小参数一致收敛的．应用牛顿拟线性法求解非线性奇异摄动时滞方程．数值实验证实了理论结果的准确性，进而表明该理论估计是稳健的．     

基于递归神经网络的多变量系统预测控制

针对线性PID控制器系数难以整定的问题，构造了一种用神经网络实现的非线性PID控制器．多个具有相同结构的非线性PID控制器并联，对多变量系统实现解耦控制器．结合预测控制的思想，提出两种控制方案．第一种是在递归多步预测的基础上，在广义最小方差目标函数下实现控制，第二种利用多步预测目标函数在线修正解耦控制器的权值．仿真实验表明这两种方法的有效性．     

快速Delaunay逐点插入网格生成算法

对插入形心的Delaunay逐点插入算法,提出按单元可插度分组的双向链表组数据结构,避免了对最大可插度单元的搜索。采用了邻接单元搜索、双向链表存储、随机方向搜索、邻接旋转、几何量继承等技术,使算法的计算时间与生成单元数近似呈线性关系,时间复杂度达到O(N1.05),N为生成单元数。算例表明,在一台AMD Athlon3200 (主频2.0GHz)PC上,该算法的四面体单元生成速度达每秒50000个以上。     

中子测井问题的有限元模拟

在测井条件下,系统研究了输运方程的Galerkin变分问题的有限元求解过程。提出了对二维、三维测井区域都适用的有限元剖分方法,在中子测井问题的有限元模拟程序中实现了稳态与非稳态问题的统一处理。计算实例验证了该方法的有效性。     

基于人工神经网络的城市航空遥感图像植被分类研究

研究了一种基于模糊量化与多层前馈神经网络相结合进行多类别遥感图像植被分类的新方法.对分类指标 进行π隶属函数模糊量化之后,进行多层前馈神经网络(MLP)训练,再用训练后的MLP网络构成监督分类器,并 以宁波市彩红外航空遥感图像资料为数据源,选取园林区为试验区,对植被进行监督分类,分类精度达78%.研究 成果表明,该方法适用于多类别的遥感图像植被分类,其分类效果也较好.