考虑移动目标不确定行为方式的轨迹预测方法

针对现有方法难以预测出符合飞行移动目标不确定行为方式轨迹的问题,提出基于逆强化学习的飞行移动目标轨迹预测方法,通过学习目标行为偏好以及模拟目标行为决策过程的方式预测目标的移动轨迹。首先基于深度神经网络建立目标的行为决策模型与行为偏好模型,然后通过最大熵逆强化学习方法交替地学习模型参数。为了有效地学习目标的不确定行为特征,采用监督学习的方法学习出目标示例轨迹概率分布模型,用于指导目标行为偏好模型的训练以及初始化目标行为决策模型,同时通过对目标行为偏好模型进行预训练的方式提高其训练质量。仿真结果表明,提出的飞行移动目标轨迹预测方法可通过学习到的目标行为决策模型较为准确地模拟目标的行为方式,预测的目标轨迹分布与真实的目标轨迹分布在Kullback Leibler(KL)散度下的相似度可达0.24。     

Force measurement using strain-gauge balance in shock tunnel based on deep learning

When a force test is conducted in a shock tunnel, vibration of the Force Measurement System (FMS) is excited under the strong flow impact, and it cannot be attenuated rapidly within the extremely short test duration of milliseconds order. The output signal of the force balance is coupled with the aerodynamic force and the inertial vibration. This interference can result in inaccurate force measurements, which can negatively impact the accuracy of the test results. To eliminate inertial vibration interference from the output signal, proposed here is a dynamic calibration modeling method for an FMS based on deep learning. The signal is processed using an intelligent Recurrent Neural Network (RNN) model in the time domain and an intelligent Convolutional Neural Network (CNN) model in the frequency domain. Results processed with the intelligent models show that the inertial vibration characteristics of the FMS can be identified efficiently and its main frequency is about 380 Hz. After processed by the intelligent models, the inertial vibration is mostly eliminated from the output signal. Also, the data processing results are subjected to error analysis. The relative error of each component is about 1%, which verifies that the modeling method based on deep learning has considerable engineering application value in data processing for pulse-type strain-gauge balances. Overall, the proposed dynamic calibration modeling method has the potential to improve the accuracy and reliability of force measurements in shock tunnel tests, which could have significant implications for the field of aerospace engineering.     

质量矩导弹的神经网络及自适应非线性控制

本文对质量矩控制导弹的动力学方程简化和控制系统的设计进行了研究。详细阐述了所提出的控制方法，介绍了径向基单隐层神经网络进行误差补偿的算法原理，用Lyapunov理论证明了所采用方法的稳定性。在给出了质量矩导弹的六自由度动力学方程和简化后的方程后，用所提出的方法设计控制系统。通过仿真分析，得出了本文所提出的方法具有较好的误差补偿能力的结论。     

神经网络在未来超分辨雷达中的应用

最新的技术发展已为研制超分辨雷达创造了条件。它能够突破普通雷达基于匹配滤波原理的分辨能力,而实现超分辨。超分辨雷达的巨大计算量通常来自求解非线性最小二乘问题时的多维搜索。本文针对这一问题,研究了雷达信号处理中的两个典型例子:(1)利用阵列处理检测个数已知而方向未知的雷达目标;(2)对波音727飞机进行超分辨距离多普勒成像。说明Hop-field神经网络通过集体运算能够求解各种困难的最优化问题,因而在未来的超分辨雷达中有广阔的应用前景。     

Design and Application of Discrete Sliding Mode Control with RBF Network-based Switching Law

This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to form the switching law in a direct and online manner with sliding-mode-related inputs and by approximating evaluation function; and the method to adjust its parameters is devised. Finally, comparable experiments are carried out to verify the application of the proposed approach to an inner-axis driven by a direct current (DC) torque motor through extra-low speed servo for a high precision flight simulator, and the axis works under deteriorating conditions such as periodically fluctuating torques of motor, nonlinear friction, and time-varying model parameters. The results show that the combined SMC can effectively improve the servo performance, for instance, to a stable 0.000 08 (°)/s speed response, the tracking error would be within 0.000 08° in 98% of operating times. Moreover, the hybrid nature of the approach imparts the RBF network the features of removing offline training and ease to set initial parameters.     

Modeling of ionospheric parameter variations in East Asia during the moderate geomagnetic disturbances

The results of modeling of ionospheric disturbances observed in the East Asian region during moderate storms are presented. The numerical model for ionosphere–plasmasphere coupling developed at the ISTP SB RAS is used to interpret the data of observations at ionospheric stations located in the longitudinal sector of 90–130°E at latitudes from auroral zone to equator. There is obtained a reasonable agreement between measurements and modeling results for winter and equinox. In the summer ionosphere, at the background of high ionization by the solar EUV radiation in the quiet geomagnetic period the meridional thermospheric wind strongly impacts the electron concentration in the middle and auroral ionosphere. The consistent calculations of the thermospheric wind permit to obtain the model results which are closer to summer observations. The actual information about the space-time variations of thermosphere and magnetosphere parameters should be taken into account during storms.     

Understanding Substorms in the Magnetotail: Early Development and Recent Progress

This is a concise review of physics of the substorm in the magnetotail. It consists of two parts. The first part summarizes historical developments in the early days of the space age (1960--1975) when the basic concepts such as magnetotail and reconnection were established and the leading model of the substorm was introduced. The second part is an overview of the research conducted in recent years (1995--2010) when very significant advances have been achieved in understanding the substorm physics by virtue of several major satellites missions that addressed the magnetotail physics intensively.     

A new bearing fault diagnosis method based on modified convolutional neural networks

Fault diagnosis is vital in manufacturing system.However,the first step of the traditional fault diagnosis method is to process the signal,extract the features and then put the features into a selected classifier for classification.The process of feature extraction depends on the experimenters’experience,and the classification rate of the shallow diagnostic model does not achieve satisfactory results.In view of these problems,this paper proposes a method of converting raw signals into twodimensi...     

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aero...     

Neural network-based fault diagnosis for spacecraft with single-gimbal control moment gyros

This paper proposes a neural network-based fault diagnosis scheme to address the problem of fault isolation and estimation for the Single-Gimbal Control Moment Gyroscopes(SGCMGs) of spacecraft in a periodic orbit. To this end, a disturbance observer based on neural network is developed for active anti-disturbance, so as to improve the accuracy of fault diagnosis.The periodic disturbance on orbit can be decoupled with fault by resorting to the fitting and memory ability of neural network. Subsequ...