基于小波神经网络的无叶扩压器失速分析

用来自不同文献的实验数据作为样本,对小波神经网络进行训练,并利用训练好的神经网络研究叶轮和扩压器几何尺寸对无叶扩压器失速的影响.计算分析结果表明:几何尺寸对于宽、窄扩压器失速的影响表现不同,这也证明了不同宽度的扩压器的确存在不同的失速机理.文章的结果,对无叶扩压器的设计具有一定的借鉴意义.      

基于人工神经网络的可压缩湍流大涡模拟模型

在国家数值风洞（NNW）工程项目的指导下，空间人工神经网络（SANN）模型被用于强可压缩湍流大涡模拟（LES）研究，其中流场的湍流马赫数分别为0.6、0.8、1.0。基于湍流的多尺度空间结构特性和人工神经网络方法发展的高精度空间神经网络（SANN）模型适用于不可压缩湍流和弱可压缩湍流。对于强可压缩湍流，流场中会出现激波结构，给大涡模拟带来了挑战。本文的研究结果表明：SANN模型适用于强可压缩湍流的大涡模拟。在先验分析中，SANN模型预测的亚格子应力和亚格子热流的相关系数超过0.995，远远高于梯度模型和近似反卷积模型等传统模型；传统模型的相对误差大于30%，而SANN模型在这方面有很大的改进，相对误差低于11%。在后验分析中，与隐式大涡模拟（ILES）、动态Smagorinsky模型（DSM）、动态混合模型（DMM）相比，SANN模型能更精确地预测能谱、各类湍流统计特性以及瞬态流动结构。因此，基于湍流多尺度空间结构特性的人工神经网络模型加深了人们对强可压缩湍流亚格子建模的认识，同时可以服务于NNW工程的流体力学模型构造。     

Development cost prediction of general aviation aircraft projects with parametric modeling

The study of the development cost of general aviation aircraft is limited by small samples with many cost-driven factors. This paper investigates a parametric modeling method for prediction of the development cost of general aviation aircraft. The proposed technique depends on some principal components, acquired by utilizing P value analysis and gray correlation analysis. According to these principal components, the corresponding linear regression and BP neural network models are established respectively. The feasibility and accuracy of the P value analysis are verified by comparing results of model fitting and prediction. A sensitivity analysis related to model precision and suitability is discussed in detail. Results obtained in this study show that the proposed method not only has a certain degree of versatility, but also provides a preliminary prediction of the development cost of general aviation aircraft.     

Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin,Eastern India

The main aim of this study is to evaluate the gully erosion susceptibility coupling the artificial intelligence and machine learning ensemble approaches. In the present study, the multilayer perceptron neural network (MLP) was used as the base classifier and the hybrid ensemble machine learning methods i.e. Bagging and Dagging were used as the functional classifiers. The Hinglo river basin, an important tributary of the Ajay River was selected as the study area, consists with the parts of Chhotonagpur plateau and Rarh lateritic region. The study area is facing the gully erosion problems which are interrupted the growth of the agriculture. The gully erosion susceptibility maps (GESMs), prepared by MLP, MLP-Bagging and MLP-Dagging were classified into four classes such as low, moderate, high and very high susceptibility classes with the help of natural break method (NBM) in GIS environment. The very high susceptibility class covered 19.41% (MLP), 13.52% (MLP-Bagging) and 15.30% (MLP-Dagging) areas of the basin. For the evaluation and comparison of the models, receiver operating characteristics (ROC), accuracy, mean absolute error (MAE) and root mean square error (RMSE) were applied. Overall, all the gully erosion susceptibility models were performed as excellent. Integration of hybrid ensemble models with MLP has increase the accuracy of the MLP models. Among these models MLP-Dagging has achieved the highest accuracy in compare to the other models. The importance of the selected factors in the present study was assessed by the Relief-F method. The results show that the soil type factor has the highest predictive performance. Sensitivity analysis also showed soil type as most important factor. The gully erosion susceptibility maps (GESMs) are considered as the efficient tool which could be used to take the necessary steps for mitigating and controlling the soil erosion problem and sustainable environmental management and development.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

Adaptive fuzzy neural network control for a space manipulator in the presence of output constraints and input nonlinearities

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this paper, a novel adaptive fuzzy neural network (FNN) control scheme is proposed for the trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of output constraints and input nonlinearities. The parametric uncertainties and external disturbances are also taken into the consideration. First, a model-based controller is designed by using the barrier Lyapunov function (BLF) to prevent the position tracking errors from violating the predefined output constraints. Then, an adaptive FNN controller is designed by using two FNNs to compensate for the lumped uncertainties and input nonlinearities, respectively. Rigorous theoretical analysis for the semiglobal uniform ultimate boundedness of the whole closed-loop system is provided. The proposed adaptive FNN controller can guarantee the position and velocity tracking errors converge to the small neighborhoods about zero, while ensuring the position tracking errors within the output constraints even in the presence of input nonlinearities. To the best of the authors’ knowledge, there are relatively few existing controllers can achieve such excellent control performance in the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control scheme.     

三轴式无人旋翼飞行器及自适应飞行控制系统设计

设计了一种操控简便的三轴式无人旋翼飞行器,由三组共轴双旋翼组成,各旋翼由直流电机直接驱动,只需调节各电机转速就能控制旋翼飞行器运动姿态和轨迹。为使三轴式无人旋翼飞行器飞行控制系统设计得到有效验证,研究了旋翼飞行器的飞行动力学非线性建模,运用叶素动量理论建立了共轴双旋翼变转速旋翼载荷计算方法,分析了旋翼入流分布对共轴双旋翼气动载荷模型的影响,通过试验验证了共轴双旋翼气动载荷计算模型的正确性。由于旋翼飞行器飞行动力学模型的非线性及未建模动力学的影响,难于建立非常精确的数学模型,给飞行控制系统设计带来了挑战。本文根据旋翼飞行器飞行动力学非线性模型推导出了旋转动力学模型逆和平移动力学模型逆控制器,利用神经网络在线自适应修正模型逆误差,采用线性PD或PI控制器调节指令跟踪误差,应用由向心回转和垂直上升组合的机动科目进行了仿真验证,给出了具有外界阵风干扰模拟的仿真结果,表明所设计的飞行控制系统具有自适应性和鲁棒性,能实现精确的轨迹跟踪控制。     

漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

 讨论了漂浮基空间机器人在轨捕获目标卫星过程的碰撞动力学建模,以及捕获操作结束后空间机器人与卫星混合体的稳定控制问题。首先采用多刚体动力学建模方法并结合空间机器人捕获目标卫星过程中的碰撞动力学特性,建立了漂浮基空间机器人在轨捕获漂浮卫星过程的动力学模型,并在此基础上计算出完成捕获操作后空间机器人与目标卫星混合体关节的运动速度。然后针对卫星及空间机器人系统惯性参数均是未知的复杂情况,应用上述模型、神经网络控制理论和Lyapunov稳定性理论,设计了空间机器人与卫星混合体在捕获过程碰撞冲击影响下稳定运动的高斯径向基函数神经网络控制方案,以达到对捕获卫星的有效控制。此外,高斯径向基函数神经网络控制方案具有不需要测量和反馈载体位置、移动速度与加速度的显著优点。系统数值仿真证实了上述控制方案的有效性。     

含模型不确定性和状态约束的航天器姿态鲁棒控制

针对严格反馈三轴稳定航天器姿态控制问题,在考虑航天器系统存在模型不确定性、未知外部扰动、系统存在时延情况下,提出了一种鲁棒控制方法。首先建立航天器误差运动学和动力学模型,使用神经网络对系统不确定性和未知扰动进行逼近、引入障碍李雅普诺夫函数对系统状态约束进行处理;然后利用反步法构造一种鲁棒自适应姿态控制器,通过李雅普诺夫方法证明闭环系统是最终一致有界的;最后,结合工程实际经验对系统已知时延进行前馈补偿。半物理仿真结果表明了所设计控制器的有效性和鲁棒性。     

基于神经网络干扰观测器的柔性航天器姿态稳定控制

本文针对柔性航天器在惯性参数未知、外界干扰、输入饱和等复杂条件下的姿态控制问题,提出了1种基于神经网络干扰观测器的柔性航天器姿态稳定控制方法。首先,基于包含压电振动抑制输入的柔性航天器姿态动力学模型,构建了包含外界干扰、惯性参数不确定性的综合扰动项;其次,基于RBF神经网络设计干扰观测器与自适应参数调节律实时地估计综合扰动;再次,设计了1种固定时间收敛且有限时间稳定的非线性滑模控制器,并通过Lyapunov理论进行了稳定性分析;最后,利用航天器闭环姿态动力学系统进行数值仿真。结果表明:所设计的基于神经网络干扰观测器的控制方法可以有效实现航天器的姿态稳定、振动抑制与干扰估计,从而顺利完成航天器的高精高稳控制任务。