涡轴发动机自适应混合诊断模型高斯加权聚类方法

针对整个飞行包线内涡轴发动机健康参数估计问题,提出基于高斯加权聚类的机载自适应混合模型建立方法.机载自适应混合模型由卡尔曼滤波器和神经网络组成,由于飞行数据样本庞大,采用高斯加权模型对涡轴发动机飞行数据进行实时聚类,利用聚类数据更新神经网络权重,并实现自适应混合模型工作范围的自动扩展.仿真结果表明了该方法的有效性,采用...     

先进战斗机的鲁棒自适应反步控制

为解决反步法在虚拟控制求导时存在计算膨胀的缺陷问题,提出了一种自适应反步控制算法.采用CMAC神经网络在线学习系统不确定性以及各阶虚拟控制量的导数信息,从而避免了系统阶次较高时引起的计算膨胀问题,并且该控制算法在实际控制输入前加入低通滤波器,避免了不连续输入可能引起的抖振问题.Lyapunov稳定性分析表明,该控制方法...     

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。     

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。     

复合神经网络在飞参缺损数据估计中的应用

针对飞参记录仪工作中存在数据缺损的现象，提出一种基于复合神经网络（CNN）对缺失数据估计的方法。仿真结果表明，运用该方法进行数据处理，可以有效地解决飞参缺损数据的问题，并且能够提高飞参数据的准确性和可信度。     

神经网络在R＆D项目中止决策中的应用

在比较研究国内外Ｒ＆Ｄ项目中止决策理论方法在基础上，论述了对正在进行的Ｒ＆Ｄ项目进行正确的中止决策模式识别的重要意义。探讨了神经网络（ＮＮ）理论用于Ｒ＆Ｄ项目中止决策的可行性，提出了适用于正在进行的Ｒ＆Ｄ项目的中止决策分析的Ｈｏｐｆｉｅｌｄ神经网络模式识别方法，并用实例验证了方法的有效性。方法的实施较为方便、计算较为简单，结果也令人满意。表明Ｈｏｐｆｉｅｌｄ神经网络模式识别方法在Ｒ＆Ｄ项目中止决策     

Regional modeling of the ionosphere using adaptive neuro-fuzzy inference system in Iran

In recent years, new techniques and algorithms such as Artificial Neural Networks (ANNs), Fuzzy Inference Systems (FIS) and Genetic Algorithm (GA) have been used as alternative statistical tools in modeling and forecasting issues. These methods have been extensively used in the field of geosciences and atmospheric physics. The main purpose of this paper is to combine FIS and ANNs for local modeling of the ionosphere Total Electron Content (TEC) in Iran. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed for TEC modeling. Also, Multi-Layer Perceptron ANN (MLP-ANN) and ANN based on Radial Base Functions (RBF) have been designed for analyzing ANFIS results. Observations of 29 Global Positioning System (GPS) stations from the Iranian Permanent GPS Network (IPGN) have been used in 3 different seasons in 2015 and 2016. These stations are located at geomagnetic low latitudes region. Out of these 29 stations, 24 stations for training and 5 stations for testing and validating were selected. The relative and absolute errors have been used to evaluate the accuracy of the proposed model. Also, the results of this paper are compared with the International Reference Ionosphere model (IRI2016). The maximum values of the average relative error for RBF, MLP-ANN, ANFIS and IRI2016 methods are 13.88%, 11.79%, 10.06%, and 18.34%, respectively. Also, the maximum values of the average absolute error for these methods are 2.38, 2.21, 1.5 and 3.36 TECU, respectively. Comparison of diurnal predicted TEC from the ANFIS, RBF, MLP-ANN and IRI2016 models with GPS-TEC revealed that the ANFIS provides more accurate predictions than the other methods in the test area.     

基于神经网络补偿的室内无人机组合导航系统

针对无人飞行器在环境特征突变情况下数据融合的可靠性大幅下降问题，提出了神经网络预测补偿的组合导航算法。首先利用扩展卡尔曼滤波和粒子滤波对激光、光流等传感器得到的数据进行融合，然后采用径向基函数（RBF）神经网络对粒子滤波前后的误差进行预测。当激光数据可靠时，RBF神经网络进行训练学习模式，当激光数据中断或者不可靠时，利用训练后的模型对系统进行误差补偿。利用无人飞行器在室内环境下进行定点和轨迹实验，结果表明补偿后的位置导航信息能够明显降低激光数据不可靠时带来的定位误差。     

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.     

三种用于加工特征识别的神经网络方法综述

加工特征自动识别技术是智能化设计与制造的关键支撑，已有的实用性算法普遍存在学习能力差、识别范围有限和识别速度慢等共性问题。神经网络方法在计算机视觉和模式识别领域获得了巨大成功，其自学习与自适应能力和高速计算等优势也已在加工特征识别中得到初步的展现。对加工特征识别中具有应用潜力的三种不同的神经网络方法进行了研究，剖析了神经网络识别加工特征中的预处理与编码和神经网络结构设计等关键性问题，分析了不同神经网络方法的异同点，总结了当前神经网络识别加工特征的发展方向，为相关领域的研究提供一定的理论指导与技术支持。