基于改进BP算法的复杂武器系统健康状态评估CSCD

根据复杂武器系统指标体系的确定原则:系统性原则、完整性原则、独立性原则、可比性原则、可操作性原则和定性与定量相结合的原则,建立了某复杂武器系统健康指标体系;分析了基本BP神经网络的优点及BP算法的缺陷,研究了一种改进的BP算法;建立了某复杂武器系统健康管理BP神经网络结构;对某复杂武器系统健康状态进行了评估,验证了指标体系的合理性。     

运动工件上字符识别系统研究

在恶劣的工业现场环境中，拍摄的工件图像会失真，为此提出了在一定噪声和模糊范围内能够正确识别字符标号的方法。工件在传送过程中可能发生偏移，使得被测件与摄像镜头的相对位置不确定，所获得的图像会发生偏转或平移，为此提出了解决字符的平移，缩放及旋转问题的方法；设计了神经网络识别系统。     

利用BP网络校正跟踪接收机的相位漂移

针对跟踪接收机不同通道之间相位差的漂移问题,提出利用BP神经网络作为校正环节来消除此相位漂移。通过原理分析用数学方法描述跟踪接收机存在的相位漂移问题,介绍BP神经网络的网络模型和一种动态全参数自适应学习算法,进而提出基于BP神经网络的相位漂移校正算法。仿真结果表明,该方法的相位校正效果较好,不仅保证了系统的角跟踪精度,而且实现了高度的自动化。     

Application of neural networks to South African GPS TEC modelling

The propagation of radio signals in the Earth’s atmosphere is dominantly affected by the ionosphere due to its dispersive nature. Global Positioning System (GPS) data provides relevant information that leads to the derivation of total electron content (TEC) which can be considered as the ionosphere’s measure of ionisation. This paper presents part of a feasibility study for the development of a Neural Network (NN) based model for the prediction of South African GPS derived TEC. The South African GPS receiver network is operated and maintained by the Chief Directorate Surveys and Mapping (CDSM) in Cape Town, South Africa. Vertical total electron content (VTEC) was calculated for four GPS receiver stations using the Adjusted Spherical Harmonic (ASHA) model. Factors that influence TEC were then identified and used to derive input parameters for the NN. The well established factors used are seasonal variation, diurnal variation, solar activity and magnetic activity. Comparison of diurnal predicted TEC values from both the NN model and the International Reference Ionosphere (IRI-2001) with GPS TEC revealed that the IRI provides more accurate predictions than the NN model during the spring equinoxes. However, on average the NN model predicts GPS TEC more accurately than the IRI model over the GPS locations considered within South Africa.     

Comparison of standard TEC models with a Neural Network based TEC model using multistation GPS TEC around the northern crest of Equatorial Ionization Anomaly in the Indian longitude sector during the low and moderate solar activity levels of the 24th solar cycle

The highest Total Electron Content (TEC) values in the world normally occur at Equatorial Ionization Anomaly (EIA) region resulting in largest ionospheric range delay values observed for any potential Space Based Augmentation System (SBAS). Reliable forecasting of TEC is crucial for satellite based navigation systems. The day to day variability of the location of the anomaly peak and its intensity is very large. This imposes severe limitations on the applicability of commonly used ionospheric models to the low latitude regions. It is necessary to generate a mathematical ionospheric forecasting and mapping model for TEC based on physical ionospheric influencing parameters. A model, IRPE-TEC, has been developed based on real time low latitude total electron content data using GPS measurements from a number of stations situated around the northern crest of the EIA during 2007 through 2011 to predict the vertical TEC values during the low and moderate solar activity levels of the 24th solar cycle. This model is compared with standard ionospheric models like International Reference Ionosphere (IRI) and Parameterized Ionospheric Model (PIM) to establish its applicability in the equatorial region for accurate predictions.     

基于神经网络的翼盒结构响应重构方法应用

翼盒结构复杂，航行中承载条件恶劣，利用有限测点信息重构其它位置响应对于实时健康监测具有很强的现实意义。通过误差反向传播神经网络训练得到响应之间的非线性关系，建立基于神经网络的响应重构方法，开展有限元分析对其进行数值仿真验证，并将该方法应用于实测随机激励环境下翼盒典型承力结构的响应重构及损伤定位与判断分析。结果表明：采用该方法重构出的预测响应功率谱密度的均方根相对误差不超过1.90 dB，主要频点误差小于10%；判断出翼盒关键测点e 的损伤或故障发生在所截取片段数据3 s 后，其故障特征频率为240 Hz 左右，该方法应用于响应重构预示及健康监测分析具有可行性。     

畸变信号重建的神经网络方法

分析了通信信道在传输信号时使信号发生畸变的现象，提出用径向基函数神经网络方法描述信道的畸变特征，并利用此方法作了仿真计算，结果表明用神经网络方法进行畸变信号重建效果较好。     

大弯角串列叶型优化设计与数值分析

以50°弯角多圆弧单列叶栅和串列叶栅为研究对象,应用数值模拟手段对所设计的两类叶栅进行数值分析,获取了其不同来流马赫数条件下的性能和流动细节。应用NSGAⅡ遗传算法结合BP神经网络技术,对串列叶栅的五个关键几何参数进行优化设计,验证了优化方法的可行性。研究结果表明:在全攻角范围内,串列叶栅的静压比都高于单列叶栅,负攻角范围内,串列叶栅损失低于单列叶栅;经过优化,串列叶栅在大负攻角下的性能略有降低,同时改善了正攻角性能,在4°攻角、0.8马赫数时静压比提升4.3%,总压损失系数降低42%;优化后串列叶栅在全工况范围内性能都要优于单列叶栅,并且串列叶栅最大压比点和最小损失点攻角均向右漂移2°。     

A comparative analysis of SLR,MLR, ANN,XGBoost and CNN for crop height estimation of sunflower using Sentinel-1 and Sentinel-2

Sustainable monitoring and determining the biophysical characteristics of crops is of global importance due to the increase in demand for food. In this context, remote sensing data provide valuable information on crops. This study investigates the relationship between the variables determined from both Synthetic Aperture Radar (SAR) and optical images and crop height. For this purpose, backscatter (σVH, σVV, σVH / σVV) and coherence (?VH, ?VV) of multi-temporal dual-polarized Sentinel-1 and vegetation indices of multi-temporal Sentinel-2 data are analyzed. Two indices, namely, Normalized Difference Vegetation Index (NDVI) and NDVI with the red-edge band (NDVIred), are interpreted to identify the contribution of the red-edge band over the near-infrared band. The Zile District of Tokat province in Turkey where dominantly sunflower cultivation is carried out, was selected as the study area. In the analysis of the data, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Artificial Neural Network (ANN), EXtreme Gradient Boosting (XGBoost), and Convolutional Neural Network (CNN) were used. In the results of the study, ANN showed the lowest RMSE = 3.083 cm (RMSE%= 11.284) in the stem elongation period. The CNN followed the lowest RMSE for the Inflorescence development and flowering stages 19.223 cm (RMSE%=15.458) and 8.731 cm (RMSE%=5.821), respectively. In the ripening period, XGBoost achieved the lowest RMSE = 8.731 cm (RMSE%=6.091). All the best models in four methods were created using common variables of σVH, σVV, ?VH, ?VV and NDVIred, except ANN which exclude coherence variables. The results concluded that NDVIred contributed more than NDVI which is widely interpreted in previous studies.     

基于神经网络的不确定性空间机器人自适应控制方法研究

提出了一种针对自由漂浮状态的空间机器人模型不确定性的神经网络自适应控制 方法。通过RBF神经网络逼近模型的非线性函数和不确定性上界,无需预先估计系统的不 确 定性程度和外部干扰,提出的自适应控制律保证了权值的有界性,解决了神经网络权值的UU B（Unknown Upper Bound）问题,即未知上界有界问题,完成了笛卡尔空间内空间机器人轨 迹规划任务。证明了所提出的控制方法的稳定性,仿真结果表明控制方法避免了对空间 机器人动力学模型的参数线性化要求降低了计算量,能够满足实际任务中的实时性要求。