基于Fuzzy ARTMAP的GPS/INS组合导航系统故障诊断

将基于双状态传播器的状态χ2检验法(SCST)结合Fuzzy ARTMAP神经网络应用于GPS/INS紧组合导航系统故障的诊断和参数识别。首先,采用双状态传播器的状态χ2检验法对组合导航系统进行故障检测,得到故障的特征模式,并给出了双状态传播器重置时间间隔选择的充分条件;然后,利用Fuzzy ARTMAP神经网络结合特定的飞行器机动对故障模式进行分类,给出了一种新的分类方法;对于飞行器按照不同轨迹进行飞行的情况,也可有效的识别故障源。最后将分类的结果送入另一个Fuzzy ARTMAP神经网络进行故障参数的估计。仿真结果表明,针对组合导航系统中陀螺、加速度计、GPS信号的一度故障,此方法能有效进行检测和隔离,并能准确估计出故障发生时间和故障幅值。     

极大似然法在飞机极曲线辨识中的应用

介绍了利用极大似然法辨识飞机极曲线的基本原理。通过仿真研究及对实测飞行试验数据的处理，表明该方法能够辨识出光滑的非线性飞机极曲线，算法具有良好的收敛性及结果的唯一性，并且可方便地推广应用于工程中其它类型的非线性曲线辨识问题。     

基于Faster-RCNN 的机场跑道异物识别定位技术研究

针对机场跑道异物(FOD) 识别检测，分析了机场跑道环境特性，建立了针对机场FOD图像识别数据库， 提出了一种基于Faster-RCNN 的机场跑道异物识别定位系统，通过无人机系统对机场跑道进行图像采集，运用 Faster-RCNN 算法框架完成异物种类识别，并结合无人机地理位置信息进行位置换算求解，得到机场FOD 的 类别信息和位置信息。经验证，该系统可高效完成机场FOD 检测识别及定位任务。     

A comparative study of decision tree,random forest,and convolutional neural network for spread-F identification

Ionospheric spread-F (SF) is a commonly observed phenomenon of electron density perturbation in the F-layer. The ionospheric irregularities structure has an adverse effect on the propagation of electromagnetic waves in the ionosphere. The automatic identification of ionospheric spread-F and statistical study of the formation of spread-F are of great significance to the study of the physical mechanism of ionospheric inhomogeneity and for prediction of ionospheric irregularities. In this paper, we describe and implement three automatic identification methods of spread-F based on machine learning: decision tree, random forest, and convolutional neural network (CNN). The performance of these automatic identification methods was verified using a large set of test data. Results show that the accuracy of all three methods on identifying ionograms with spread-F exceeded 90%. After comparing the results of the three methods, we found that the decision tree method was the simplest and with the structure easiest to be understood, and it required the shortest interpretation time. In terms of the identification results, the random forest method provided better results than the decision tree method, and the CNN method was the best at accurately identifying ionograms with spread-F.     

小波变换在动力学系统识别中的应用

试图用小波变换来讨论动力学系统识别中的几个问题，并用以研究在系统分析中利用小波变换这个工具的可能性。只涉及线性时不变结构的动力学系统，它应该适用于非线性或线性时变系统的分析和识别。做为一个具体应用，给出了使用小波变换识别模态阻尼系数的模拟实侧，可用于颤振数据分析。     

GNSS最优载波相位平滑伪距研究

平滑常数是影响载波相位平滑伪距精度的关键参数,实际数据处理时主要依据经验设定平滑常数。这种主观设定过程缺乏理论依据,无法达到最优平滑效果。针对此问题,以适用于实时GNSS载波相位平滑伪距的经典Hatch递推滤波算法为基础,在连续时间域上分析了载波相位平滑伪距误差的主要构成,给出了总误差估算公式,分析了平滑常数对平滑精度的影响传导机制。进一步,采用令平滑总误差最小为目标的极值法推导给出了最优载波相位平滑常数的计算公式,给出了最优载波相位平滑伪距的完整处理步骤。最优平滑常数算法在数学意义上最优,大幅压缩了伪距测量误差,又不会引入过大的电离层发散误差。通过两个实际算例,证明了算法有效性。     

PARAMETER IDENTIFICATION METHOD OF NONLINEAR JOINTS

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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.     

基于LSTM和CNN的高速柱塞泵故障诊断

针对高速轴向柱塞泵容易发生空化，且目前空化故障诊断方法存在依赖手工特征提取、鲁棒性不高的问题，提出了一种基于长短时记忆（LSTM）和一维卷积神经网络（1D-CNN）相结合的空化故障诊断方法。搭建了柱塞泵故障实验台，采集柱塞泵在不同空化等级下的壳体振动信号。利用LSTM和1D-CNN搭建的分类模型对不同进口压力情况下的振动信号进行空化等级识别。实验结果表明：提出的方法能够准确地识别出4类不同的空化等级，准确率高达99.5%，同时在不附加降噪方法的情况下，具有良好的鲁棒性，在0 dB信噪比的情况下，识别准确率高达87.3%。     

企业文化定量评价方法的研究

针对目前评价企业文化的方法在定量分析方面的不足，应用模糊数学理论建立了一个企业文化模糊综合评价模型是一个企业文化产例加以说明，为企业家决策提供科学依据。