企业网络：类型比较及发展研究

分析了作为中间组织类型的企业网络所具有的组织结构和组织特征，并对其进行了三个层次的划分：集群层次、集团层次和联盟层次，比较分析了处于各个不同层次上的企业网络之间的差异性。认为，技术平台和合作平台将成为企业网络今后发展和进化的关键，并探讨了其相应的两个方面：虚拟创新和信任。     

BP网络进行0-9十个数字图像的识别

在BP算法和数字图像处理技术的基础上,本文在MATLAB软件环境下提取Word文档中26种字体的0-9十个数字的图像,使用MATLAB神经网络工具箱及图像处理工具箱进行数字的识别,给出了较详细的处理步骤及相关程序,并比较了各种识别算法的收敛速度和识别率。     

基于模块度的复杂网络社团结构聚类新算法

针对传统的分裂聚类算法删除连边会影响节点划分的问题,结合K-means算法的思想并加以改进,提出了一种以邻居节点为聚类备选集,所有社团中心节点同时聚类的同步聚类算法。该算法根据节点中心度和最短路变化率确定社团中心节点集,然后以中心节点为社团中心,以邻居节点为聚类备选集合进行聚类,完成社团的划分。将算法应用到Zachary网络中并与GN算法、Top Leader算法进行比较,仿真结果表明该算法对网络有较好的划分。     

基于BP网络的机组工作量评估方法研究

为研究机组工作量的评估问题,搭建了模拟飞行实验平台,设定了7种不同负荷的飞行任务。模拟飞行中,对被试的生理指标进行测量,并采集其主观评价值。采用BP网络进行数学建模,将生理指标测量值作为输入、NASA-TLX表法的评价值作为输出,利用输入、输出模式对对网络进行训练,并对该方法进行验证。结果表明,基于BP网络的机组工作量评估方法,较传统方法更为稳定、精确,且大大降低了被试数量。提高训练模式对的数量、简化生理指标的测量过程,并保证测量精度是该方法成功的关键。     

Phasing with near rectilinear Halo orbits: Design and comparison

In the next future, space agencies are planning to return to the Moon. The objective is to assemble an orbiting space station, called Gateway, on a Near Rectilinear Halo Orbit around the Moon as a base for future Moon and deep space missions. Within this framework, multiple side missions will be planned to sustain the Gateway (Artemis mission). The proposed work is thought in framework of the preliminary design of future cargo missions, in particular on the design of an efficient phasing trajectory, under the Circular Restricted Three body problem hypotheses, to bring a cargo vehicle from the end of the Earth-Moon transfer to the beginning of the proximity operations such as rendezvous and docking with the space station. The work aims covering the lack of literature in phasing trajectories with the NRHO by proposing three different strategies to connect the Earth-Moon transfer trajectory with the proximity operations. The three strategies are classified based on the choice of the parking orbits or the choice of the manifolds. Two strategies use butterfly and Halo orbits to park the vehicle before transferring to the target orbit. The third strategy, instead, uses manifolds to allow a direct phasing. In the paper, the three innovative strategies are designed and compare in a specific scenario.     

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.     

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...