Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models from limited input–output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. In this study, the process of the Dst index is treated to be a structure-unknown system, where the solar wind parameter (VBs) and the solar wind dynamic pressure (P) are the system inputs, and the Dst index is the system output. A novel multiscale RBF (MSRBF) network is introduced to represent such a two-input and single-output system, where the Dst index is related to the solar wind parameter and the dynamic pressure, via a hybrid network model consisting of two submodels: a linear part that reflects the linear relationship between the output and the inputs, and a nonlinear part that captures the effect of the interacting contribution of past observations of the inputs and the output, on the current output. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using a forward orthogonal regression (FOR) algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems.     

MPP蛀孔互联网的模拟研究

蛀孔交换技术的特性不同于存储转发交换技术的特性。该文分析了消息在重载蛀孔网络中传输的性能特性，指出了在典型的二维MESH网中应用蛀孔技术的障碍。在此基础上提出了二维MESH的一种改进结构，即间接二维MESH结构，并分析、模拟了该结构在各种网络负载及各种网络规模下的性能特性。分析及模拟都说明了该结构的高效性。     

基于使命任务分解的航空电子跨平台通信组织与仿真

在先进的综合化航空电子系统设计中,跨平台通信的组织将直接体现使命任务的需求。通过功能流程框图将航空电子系统使命任务各个阶段分解为功能组件,给出了一种功能组件之间跨平台信息交互的组织方法,并采用移动自组织通信的方式实现。将某任务片段中的空中图像信息传输作为模拟案例,使用OPNET软件进行仿真研究。仿真结果表明,该方法根据任务的特性设定平台之间的动态通信过程,与没有经过使命任务分解设计的对照组相比,在同等发射功率下能够有效地降低误码率。     

用于测频测向的偶对称神经网络模型

介绍了Hopfield神经网络用于测频测向的原理。为解决一些基本算法的局部极值问题，建立了偶对称神经网络的模型，并给出了相应的算法。理论分析和仿真结果表明，该算法用于解决全局优化问题是有效的，可用于解决测频测向问题。     

基于贝叶斯网的业务端编程方法

针对现有的业务端编程方法缺乏对于没有软件开发背景的最终用户的指导机制,提出了一种基于贝叶斯网的业务端编程方法.首先提出了一种基于贝叶斯网的最终用户编程基本框架,然后基于贝叶斯网结构学习方法和过程日志建立了用来支撑业务端编程的领域专家系统,在此基础上提出了以领域专家系统为核心的启发式业务端编程方法.该方法在编程过程中依赖领域专家系统对最终用户推荐业务活动,提供实时指导,帮助最终用户逐步完成应用系统的构造.最后对该方法的有效性进行了定性的评估和分析,结果表明,基于贝叶斯网的业务端编程方法可以在一定程度上提高最终用户编程的效率.     

快速伺服刀架神经网络自适应PID控制

为了提高快速伺服刀架的控制性能,减小跟踪误差,实现正弦网格表面精密加工,提出了基于RBF和BP神经网络的自适应PID控制策略.由仿真结果可以看出,采用基于神经网络的自适应控制算法,使跟踪误差的最大值降低为1.37μm,跟踪误差的绝对均值降低为0.52μm.这两项指标相对与传统PID控制分别降低了28%和40%.     

基于自校正模糊神经控制的无刷直流传动系统

提出了一种自校正模糊神经网络控制器(SCFNNC)来实现无刷直流电动机起动、调速、制动等各运行阶段的性能指标.该SCFNNC是采用调整系统增益参数的方法完成较完善的控制规则的.重点研究了系统自校正增益参数的确定方法,模糊控制器的设计,人工神经网络实现模糊控制规则的方法等.自校正增益参数是根据系统对超调量、转速稳态误差、动态速降的期望值来确定的.设计模糊控制器时是根据系统的性能指标,确定出合适的模糊控制规则表,用于训练神经网络.为使系统的性能达到最佳,采用了自校正模糊神经控制、开关控制和比例控制相结合的复合控制方法,通过数学仿真证实配备SCFNNC的系统具有优良的动、静态特性,及较强的鲁棒性.     

基于ANN的故障诊断专家系统的应用研究

介绍一种基于ANN的设备故障智能诊断专家系统模型,探讨了该模型的基本结构和相应的推理机制及故障诊断策略,提出将设备故障分为低层故障和高层故障的思想以及相应的分层诊断、分块进行的诊断策略,并以MK9-5卷接机组为诊断对象,研究了该模型及诊断方法在设备故障智能诊断中的具体应用.     

矢量喷管数学模型研究

采用计算流体力学的有限体积法和神经网络方法,对矢量喷管的统一数学模型进行了研究,并得到了一种以神经网络描述形式的数学模型。该模型为矢量喷管控制规律和带有推力矢量的飞行／推进系统综合控制的研究提供了一定基础。     

A new global F2 peak electron density model for the International Reference Ionosphere (IRI)

A new neural network (NN) based global empirical model for the F2 peak electron density (NmF2) has been developed using extended temporal and spatial geophysical relevant inputs. Measured ground based ionosonde data, from 84 global stations, spanning the period 1995 to 2005 and, for a few stations from 1976 to 1986, obtained from various resources of the World Data Centre (WDC) archives (Space Physics Interactive Data Resource SPIDR, the Digital Ionogram Database, DIDBase, and IPS Radio and Space Services) have been used for training a NN. The training data set includes all periods of quiet and disturbed magnetic activity. A comprehensive comparison for all conditions (e.g., magnetic storms, levels of solar activity, season, different regions of latitudes, etc.) between foF2 value predictions using the NN based model and International Reference Ionosphere (IRI) model (including both the International Union of Radio Science (URSI) and International Radio Consultative Committee (CCIR) coefficients) with observed values was investigated. The root-mean-square (RMS) error differences for a few selected stations are presented in this paper. The results of the foF2 NN model presented in this work successfully demonstrate that this new model can be used as a replacement option for the URSI and CCIR maps within the IRI model for the purpose of F2 peak electron density predictions.