A high‐order upwind control‐volume method based on integrated RBFs for fluid‐flow problems

This paper is concerned with the development of a high‐order upwind conservative discretization method for the simulation of flows of a Newtonian fluid in two dimensions. The fluid‐flow domain is discretized using a Cartesian grid from which non‐overlapping rectangular control volumes are formed. Line integrals arising from the integration of the diffusion and convection terms over control volumes are evaluated using the middle‐point rule. One‐dimensional integrated radial basis function schemes using the multiquadric basis function are employed to represent the variations of the field variables along the grid lines. The convection term is effectively treated using an upwind scheme with the deferred‐correction strategy. Several highly non‐linear test problems governed by the Burgers and the Navier–Stokes equations are simulated, which show that the proposed technique is stable, accurate and converges well. Copyright © 2010 John Wiley & Sons, Ltd.     

BP网络和RBF网络在期货预测中的比较研究

通过仿真实例,对BP网络和RBF网络在期货预测应用上的表现性能进行了比较研究,仿真结果表明,BP网络更适合于期货市场价格预测.实际的期货预测应用中,此结论可指导神经网络模型的选择.     

A numerical method for one‐dimensional nonlinear Sine‐Gordon equation using collocation and radial basis functions

In this article, we propose a numerical scheme to solve the one‐dimensional undamped Sine‐Gordon equation using collocation points and approximating the solution using Thin Plate Splines (TPS) radial basis function (RBF). The scheme works in a similar fashion as finite difference methods. The results of numerical experiments are presented and are compared with analytical solutions to confirm the good accuracy of the presented scheme.© 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2008     

Numerical study of radial basis function interpolation for data transfer across discontinuous mesh interfaces

Allowing discontinuous or non‐matching mesh spacing across zonal interfaces within a computational domain offers many advantages, particularly in terms of easing the mesh generation process, reduction of required mesh densities, and relative motion between mesh zones. This paper presents a numerical study of a universal method for interpolating solution data across such interfaces. The method utilises radial basis functions (RBFs) for n‐dimensional volume interpolation, and treats the available solution data points simply as arbitrary clouds of points, eliminating all connectivity requirements and making it applicable to a wide range of computational problems. Properties of the developed meshless interface interpolation are investigated using analytic functions, and three issues are considered: the achievable order of spatial accuracy of the RBF interpolation alone and comparison with a variable order polynomial; the effect of a combined RBF and polynomial interpolation; and the ability of the method to recover frequency content. RBF interpolation alone is shown to achieve fourth‐order to sixth‐order spatial accuracy in one and two dimensions, and in three dimensions, using a small number of data points, third‐order and above is achievable even for a 3 : 1 discontinuous cell spacing ratio, that is a 27 : 1 volume ratio, across the interface. Hence, it is inefficient to include polynomial terms, since improving on the RBF spatial accuracy results in a significant increase in the system size and deterioration in conditioning. It is also shown that only five points per wavelength are required to capture both frequency and amplitude content of periodic solutions to less than 0.01% error.Copyright © 2013 John Wiley & Sons, Ltd.     

基于模糊神经网络的MVP评价模型及实证分析

利用模糊数学和神经网络方法建立对运动员进行评价的模糊网络模型,采用NBA流行的各评价指标作为其输入,模糊综合评价结果作为输出。样本数据采用2003~2004赛季NBA各单项50强的常规赛数据,分别用BP网络和RBF网络,建立分析系统,比较结果证明RBF网络仿真效果最好,完全可以实用,该模型也可以用在其它综合评价系统中。     

径向基函数神经网络在股票走势模式分类中的应用

探讨了径向基函数神经网络在个股走势模式分类中的应用问题。提出了一种可调基宽度的计算方法和若干数据预处理的措施。实例计算表明，该方法取得了较好的效果     

一种关于汽车整车不确定需求的组合预测模型

提出了一种针对汽车生产企业整车不确定需求的组合预测模型,即利用神经网络拟合数据基本变化趋势,然后对企业产品的市场占有率进行评价,以调节需求变化趋势.应用实例,对模型进行了验证,说明该模型能够较好地诠释企业产品需求信息,具有较好的预测能力.     

基于神经网络的多元稀土萃取组分含量的软测量

提出了一种应用RBF神经网络建立多组分稀土萃取平衡模型的方法,通过与萃取过程物料平衡模型相结合给出了在线预测稀土申级萃取分离生产过程中各组分含量的软测量方法。通过现场操作运行实测数据的建模仿真验证,表明这种混合软测量方法是有效的。     

基于RBF网络的光纤荧光海水叶绿素a含量在线监测系统的研究

介绍了能够实现对海水中叶绿素a含量在线监测的光纤荧光系统。系统将荧光技术、光纤技术和基于RBF(Radial Basis Function Neural Network)神经网络的标定方法相结合，建立了用于海水叶绿素a含量在线监测的最佳RBF网络结构，系统具有结构简单，探头无源及高灵敏度等特点。     

基于径向基函数神经网络的未知模型混沌系统控制

基于径向基函数神经网络的智能方法对混沌进行控制-该方法不需要被控混沌系统的解析模型，控制的目标可以为周期轨道，也可以为连续变化的目标函数，在模型参数发生摄动和存在测量噪声情况下，控制仍然有效-研究了神经网络误差对控制精度的影响，并给出相关的定理及证明-针对Logistic映射和Henon吸引子的仿真结果，表明了此方法的有效性和可行性- 关键词： 混沌控制 径向基函数神经网络 参数摄动 测量噪声