Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

TS-InSAR analysis for monitoring ground deformation in Lanzhou New District,the loess Plateau of China,from 2017 to 2019

Large-scale land creation projects involving the cutting of mountains to infill gullies for construction have been carried out in Lanzhou New District (LZND). However, there is an urgent need for comprehensive and detailed research on the spatiotemporal evolution of ground deformation in LZND. Based on Sentinel-1A SAR data, combined with the urban geological background, the ground deformation in LZND from 2017 to 2019 was analysed. Two independent, multi-temporal techniques, persistent scatterers interferometry (PS-InSAR) and the small baseline subset (SBAS-InSAR), were used to calculate the deformation time series, and the results were cross-verified. The time series-monitoring results of the SBAS and PS calculations exhibited strong consistency in LZND and verified the high reliability of the experimental results. The results showed the whole surface of the LZND from March 2017 to October 2019 maintained stability, and the deformation rate was primarily in the range of ?10 to 10 mm/year. However, ground deformation in the Xicha area was evident. The maximum annual deformation rates monitored by SBAS-InSAR and PS-InSAR were ?52.48 mm/year and ?56.35 mm/year, respectively. The most typical deformation areas include the built-up area and the land creation area. The surface subsidence area was concentrated in the filling area. The ground deformation range of LZND kept expanding and accelerating from 2017 to 2019. Land creation, urban construction, geology and precipitation were the primary factors contributing to local severe ground deformation. The results of this study provide reference for the regional urban planning in LZND.     

适应拓扑变化的拥塞最小化网络更新策略

软件定义网络的出现推动了航空集群机载网络的发展，同时也为机载网络的一致性更新带来了挑战。针对拓扑变化导致的网络更新期间可用路径失效问题，提出适应拓扑变化的拥塞最小化网络更新策略。首先，为减少可用路径失效带来的数据包丢失，在更新之前采用重路由机制进行处理；然后，针对重路由机制可能引起的网络拥塞，设计了贪婪流迁移算法缓解拥塞；最后，通过瞬时拥塞最小化更新算法完成整个网络更新。仿真结果表明：与传统的拥塞一致性更新算法相比，所提更新策略用规则开销、瞬时网络拥塞方面的少量提高换取了网络更新期间数据包丢失的显著降低。     

Spatial modeling of flood probability using geo-environmental variables and machine learning models,case study: Tajan watershed,Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.     

面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。     

不确定航迹自适应预测模型

针对现有航迹预测技术中，存在无模技术缺乏理论分析支持、能力有限、适用范围窄、有模技术先验假设过多、前提条件严苛、通用性差等问题，通过理论推导，利用循环和多层神经网络结构，研究提出了具有理论严谨、先验假设少、适用范围广、通用性强等优点的不确定航迹自适应预测模型，同时给出了典型的实现方法。该模型克服了现有航迹预测方法的缺点与不足，并具有无模与有模两类技术的优点与长处，仿真和实测实验验证表明：该模型能很好地提取识别出数据中存在的模式，并基于模式，进行正确有效地预测，能有效解决不同实际环境中的航迹预测问题，效果明显。     

Monthly trends in temporal and spatial distribution of Ionospheric Irregularities across the African region during the descending phase of solar cycle 24

Ionospheric irregularities are well-known phenomena associated with ionospheric scintillation. These irregularities comprise steep electron density gradients in the equatorial F region some 1 to 2 h after sunset in regions close to the geomagnetic equator. Using the IGS network of GNSS receivers spread across the low-latitude region over the African sector, we present the monthly trends in ionospheric irregularity activity levels based on Rate of TEC Index (ROTI) during the declining phase of solar 24. The monthly trends are statistically represented by counts of the night time ROTI values exceeding a threshold of 0.4 TECU/min. A clear trend emerges on the irregularity occurrence across the African sector: during the first four months of the year, the irregularity occurrence is highly pronounced on the western side of the region. The irregularity occurrence then shifts to the eastern side during the months of May, June, July, and August. During the last four months of the year, the irregularity occurrence is again more intense on the western side of Africa than on the eastern side. The occurrence of irregularity structures on only one side of the region during a given night is an unusual feature reported here for the first time.     

基于神经网络和人工势场的协同博弈路径规划

协同博弈路径规划是空战自主决策、机器人体育比赛等应用场景中的重要问题，其难点在于对环境对抗性反馈的实时自适应和多智能体的相互配合。提出一种基于神经网络和人工势场的协同博弈路径规划方法，使用反向传播（BP）神经网络自适应调整人工势场函数系数，并将人工势场作为神经网络输出端的特征提取。为解决真实样本质量和数量不足的问题，基于遗传算法仿真生成样本数据用于神经网络训练，并通过滚动时域的思路面向动态博弈优化样本性能。从样本数据中提炼出距离差与航向差以反映协同和博弈特性，利用神经网络的黑盒特性和学习能力解决协同博弈问题。应用于二对一反隐身超视距空战路径规划，比经典人工势场法有明显性能提升，且计算开销可接受，计算复杂度分析表明该方法可以较好扩展到多机对抗场景。     

网络文化境遇下大学生党员教育的研究

随着科技的进步，互联网的发展，网络文化成为当代全新、独立而又重要的文化表达形态。网络为人的发展与需要提供了无限的机会，也对我国当代大学生的价值观念、理想信念等产生了深刻的影响。因此，我们应正确认识网络文化的利弊，分析其对大学生党员教育的影响，提升大学生党员教育的实效性，引领党建工作创新开展。     

基于随机网络演算的TTE网络时延分析

时间触发以太网(TTE)是一种新颖的混合型时间触发和事件触发的通信网络,通过引入时间触发(TT)流量,增强了航空电子全双工(AFDX)交换式以太网的确定性。虽然TT流量具有完全的时间确定性,但是与AFDX中虚拟链路(VL)兼容的速率约束(RC)流量仍具有一定非确定性。传统用于AFDX网络实时性能分析的方法在考虑TT流量固定分区调度时隙的影响下已不再适用,为了保障RC流量的实时性能,分别提出了基于确定性网络演算和随机网络演算两种延迟分析模型。在确定性网络演算下,通过构造TT流量的聚合到达曲线和RC流量的服务曲线以得到RC的确定性延迟上界;在随机网络演算下,通过切诺夫(Chernoff)边界定理构造RC流量的两状态伯努利分布模型,得到概率保证下的延迟上界。对比实验结果表明:随机网络演算模型可以有效减小确定性网络演算模型对RC流量性能分析的悲观性,同时从一定程度上验证了两种理论分析模型的正确性。