人体头部受撞击脑损伤界限的研究

脑损伤的研究，是生物力学研究的重要问题之一，其研究涉及到医学、力学的很多方面。本文将承受撞击的颅脑看成一个整体，同时又考虑其结构和力学性质。采用粘弹性的Kelvin体进行计算分析，比较分析三种不同的判据对实际人脑的损伤的判断的影响。     

一种具有较强泛化能力的神经网络模型研究与应用

分析了影响神经网络模型泛化能力的因素。以电解铝过程中氧化铝浓度的神经网络软测量为例 ,提出了利用先验知识确定网络结构 ,采用特定实验保证样本数量和质量 ,离线训练加在线学习修正模型等措施 ,改善神经网络模型的泛化能力。现场应用表明这些措施是有效的。这样建立的神经网络模型准确 ,泛化能力强 ,为实现过程的先进控制提供了可靠保障     

靶场外测数据野值点的统计诊断技术

本文修正了野值点的数学描述性定义，建立了野值的分类模型，创立了多个野值点及野值斑点的“稳健－似然比”检验技术，并进行了仿真计算。     

虚拟样机技术在双模制导系统研制中的应用

将虚拟样机技术用于双模制导系统的研制。以Pro／E为三维实体特征建模工具，MSC、ADAMS为运动学和动力学分析工具，Matlab为控制分析软件，对机械系统和控制系统进行联合分析和调试。给出了机械模型、控制模型和两者间的输入输出，以及目标运动模型的建立方法。试验结果表明，所设计的虚拟样机能用于动态校核、参数优化、回归设计和演示验证，并能进行运动分析和干涉检测、跟踪以及交班试验等。     

基于齿数的渐开线直齿轮参数化建模

介绍了在UG环境下渐开线直齿轮参数化建模的方法,分析了不同齿数范围对建模过程的影响,并依据分析结果建立了渐开线直齿轮的两个精确模型,利用这两个模型通过修改参数列表能快捷得到满足任意参数要求的渐开线直齿轮的三维模型。     

基于ARMA模型在线辨识器的线性系统建模方法

线性系统的在线辨识是系统辨识领域中最感兴趣的问题之一。本文给出变阶式递推增广的最小二乘法及阶估计准则。在此基础上 ,给出一类ARMA模型在线辨识器。这种辨识器可用于在线或离线辨识。实例表明 ,建模计算量大大减小 ,并具有较高的精度     

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection (PS) algorithm and multi-input multi-output recursive reduced least square support vector regression (MRR-LSSVR) machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression (LSSVR) machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5&. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.     

Identification method for helicopter flight dynamics modeling with rotor degrees of freedom

A comprehensive method based on system identification theory for helicopter flight dynamics modeling with rotor degrees of freedom is developed. A fully parameterized rotor flapping equation for identification purpose is derived without using any theoretical model, so the confidence of the identified model is increased, and then the 6 degrees of freedom rigid body model is extended to 9 degrees of freedom high-order model. Bode sensitivity function is derived to increase the accuracy of frequency spectra calculation which influences the accuracy of model parameter identification. Then a frequency domain identification algorithm is established. Acceleration technique is developed furthermore to increase calculation efficiency, and the total identification time is reduced by more than 50% using this technique. A comprehensive two-step method is established for helicopter high-order flight dynamics model identification which increases the numerical stability of model identification compared with single step algorithm. Application of the developed method to identify the flight dynamics model of BO 105 helicopter based on flight test data is implemented. A comparative study between the high-order model and rigid body model is performed at last. The results show that the developed method can be used for helicopter high-order flight dynamics model identification with high accuracy as well as efficiency, and the advantage of identified high-order model is very obvious compared with low-order model.     

惯组中光纤陀螺温度漂移的二维插值建模研究

光纤陀螺对温度较为敏感,输出受温度及温度变化率影响严重,在实际工作中需要对温度漂移误差进行建模补偿。传统多项式拟合方法如最小二乘法,无法很好地满足精度要求。因此,首先对光纤陀螺工作原理与温度漂移误差产生原理进行分析,得出光纤陀螺温度漂移误差特性。利用传统多项式模型对不同温度下启动的光纤陀螺进行建模补偿,得到补偿后的精度并不理想。利用新的二维插值模型对上述试验重新进行建模补偿,结果表明二维插值模型明显优于多项式模型,光纤陀螺的零偏稳定性由补偿前的0.0153(°)/h提高到0.0051(°)/h,有利于工程上应用。