贝叶斯网络在飞控系统可靠性评估中的应用

把一种基于贝叶斯网络的故障树分析法应用于飞控系统俯仰控制功能的可靠性评估中,突破传统故障树分析法的局限性——各部件独立、状态为二值,不但可以计算系统的可靠性指标,而且可以定量给出某个事件或某几个事件在系统可靠性中所占的地位,找出系统的薄弱环节。     

Neural network development for the forecasting of upper atmosphere parameter distributions

This paper presents a neural network modeling approach to forecast electron concentration distributions in the 150–600 km altitude range above Arecibo, Puerto Rico. The neural network was trained using incoherent scatter radar data collected at the Arecibo Observatory during the past two decades, as well as the Kp geomagnetic index provided by the National Space Science Data Center. The data set covered nearly two solar cycles, allowing the neural network to model daily, seasonal, and solar cycle variations of upper atmospheric parameter distributions. Two types of neural network architectures, feedforward and Elman recurrent, are used in this study. Topics discussed include the network design, training strategy, data analysis, as well as preliminary testing results of the networks on electron concentration distributions.     

A New Exposed-terminal-free MAC Protocol for Multi-hop Wireless Networks

This article presents a new multichannel medium access control (MAC) protocol to solve the exposed-terminal (ET) problem for efficient channel sharing in multi-hop wireless networks. It uses request-to-send and clear-to-send (RTS/CTS) dialogue on a common channel and flexibly opts for conflict-free traffic channels to carry out the data packet transmission on the basis of a new channel selection scheme. The acknowledgment (ACK) packet for the data packet transmission is sent back to the sender over another common channel thus completely eliminating the exposed-terminal effects. Any adjacent communication pair can take full advantage of multiple traffic channels without collision and the spatial reuse of the same channel is extended to other communication pairs which are even within 2 hops from them. In addition, the hidden-terminal effect is also considerably reduced because most of possible packet collisions on a single channel are avoided due to traffic load balance on multichannels. Finally, a performance comparison is made between the proposed protocol and other typical MAC protocols. Simulation results evidence its obvious superiority to the MAC protocols associated with other channel selection schemes and traditional ACK transmission scheme as well as cooperative asynchronous multichannel MAC (CAM-MAC) protocol in terms of four performance indices: total channel utilization, average channel utilization, average packet delay, and packet dropping rate.     

Towards a GPS-based TEC prediction model for Southern Africa with feed forward networks

In this paper, first results from a national Global Positioning System (GPS) based total electron content (TEC) prediction model over South Africa are presented. Data for 10 GPS receiver stations distributed through out the country were used to train a feed forward neural network (NN) over an interval of at most five years. In the NN training, validating and testing processes, five factors which are well known to influence TEC variability namely diurnal variation, seasonal variation, magnetic activity, solar activity and the geographic position of the GPS receivers were included in the NN model. The database consisted of 1-min data and therefore the NN model developed can be used to forecast TEC values 1 min in advance. Results from the NN national model (NM) were compared with hourly TEC values generated by the earlier developed NN single station models (SSMs) at Sutherland (32.38°S, 20.81°E) and Springbok (29.67°S, 17.88°E), to predict TEC variations over the Cape Town (33.95°S, 18.47°E) and Upington (28.41°S, 21.26°E) stations, respectively, during equinoxes and solstices. This revealed that, on average, the NM led to an improvement in TEC prediction accuracy compared to the SSMs for the considered testing periods.     

Progress towards a new global foF2 model for the International Reference Ionosphere (IRI)

A new neural network (NN) based global empirical model for the foF2 parameter, which represents the peak electron density has been developed using extended temporal and spatial geophysical relevant inputs. The first results from this new model were presented at the International Reference Ionosphere (IRI) 2006 workshop in Buenos Aires, Argentina, and showed that this new model would be a suitable replacement for the URSI and CCIR maps currently used within the IRI model for the purpose of F2 peak electron density predictions. Measured ground based ionosonde data, from 85 global stations, spanning the period 1995–2005 and, for a few stations from 1976 to 1986, obtained from various resources of the World Data Centre (WDC) archives (Space Physics Interactive Data Resource SPIDR, the Digital Ionogram Database, DIDBase, and IPS Radio and Space Services) have been used for training a NN.     

时滞细胞神经网络平衡点的全局指数稳定性研究

利用常数变易法和变量替换的方法，研究了具有时滞的细胞神经网络平衡点的奎局指数稳定性，给出了网络平衡点的全局指数稳定性的充分判据。     

空中交通网络管理的概念与策略研究

提出以空中交通网络为主要研究对象的解决空中交通管理的思路,定义了空中交通网络的概念,并对空中交通网络管理的策略和机制进行了探讨。     

采用BP网络辨识航空发动机数学模型

运用ＢＰ网络和实测数据作为学习样本，对某型航空发动机的数学模型进行了辨识研究。辨识模型输出的结果与实测数据比较误差较小。这种方法收敛速度快、精度高，结果表明用ＢＰ网络辨识方法能够得到比较精确的发动机数学模型。     

无人直升机的自适应非线性控制

最近几年，许多仿真结果都揭示了基于神经网络的直接自适应控制在飞行控制系统设计中的发展潜力。其中最重要的一点就是显著降低了对系统动态模型的要求。本文把该方法应用于无人直升机，给出了自适应姿态控制系统和跟踪控制器的设计方法。通过，数字仿真表明该控制方法有良好的跟踪控制性能。     

基于遗传算法的神经网络优化

针对神经网络结构的设计基本上依赖于人的经验的缺点，提出了一种利用遗传算法优化神经网络的算法。该算法结合了神经网络的快速并行性和遗传算法的全局搜索性，对神经网络的连接权和结构进行了优化，剔除整个网络冗余节点和冗余连接权，提高了网络的处理能力。通过实例证明该算法具有很高的精度，在机械故障诊断中具有良好的应用前景。