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。     

有线电视HFC网络与光纤链路设计

本文论述了双向传输有线电视ＨＦＣ网络结构并比较了其优缺点，文中还就工程应用的实例讨论了光纤链路的设计问题。     

基于神经网络的智能刀具状态检测系统

可靠的在线刀具磨损状态检测是柔性制造系统、计算机集成制造系统以及自动化机床必不可少的一个环节。文中论述了用反传神经网络与一类模糊神经网络分析处理由力传感器和声发射传感器所测得的刀具状态信号，识别出刀具的磨损情况，从而进一步实现刀具磨损状态的在线检测，控制自动机床及时更换刀具。本研究对四种规格的钻头的磨损情况进行了全程检测，并比较分析了反传神经网络与模糊神经网络对这一问题的有效性。实验结果表明，这两种方法对处理刀具磨损状态检测均有显著的效果与很高的准确性。用一类模糊神经网络处理多传感器信息是实现刀具状态在线检测的一个极为有效的方法。     

基于Hamming神经网络聚类分析的进化策略

本文提出了一种基于Ｈａｍｍｉｎｇ神经网络聚类分析的进化策略，模糊自适应Ｈａｍｍｉｎｇ神经网络各类族的权重矢量纪录被进化搜索过的区域，并相应妄下该区域内最优个体和它的适应度，因此通过Ｈａｍｍｉｎｇ神经网络对进化个体的聚类分析，进化策略具有搜索记忆性，可以充分保证下一代遗传群体中个体遗传基因的丰富性，从而避免早熟现象的发生，这种进化策略还可以避免在被搜索过的区域内的无用搜索，进而加快进化策略的收敛速度     

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.     

Using the radial basis function neural network to predict ionospheric critical frequency of F2 layer over Wuhan

Neural networks (NNs) have been applied to ionospheric predictions recently. This paper uses radial basis function neural network (RBF-NN) to forecast hourly values of the ionospheric F₂ layer critical frequency(f_oF₂), over Wuhan (30.5N, 114.3E), China. The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension. The whole study is based on a sample of about 26,000 observations of f_oF₂ with 1-h time resolution, derived during the period from January 1981 to December 1983. The performance of RBF-NN is estimated by calculating the normalized root-mean-squared (NRMSE) error, and its results show that short-term predictions of f_oF₂ are improved.     

An artificial neural network approach for aerodynamic performance retention in airframe noise reduction design of a 3D swept wing model

With the progress of high-bypass turbofan and the innovation of silencing nacelle in engine noise reduction, airframe noise has now become another important sound source besides the engine noise. Thus, reducing airframe noise makes a great contribution to the overall noise reduction of a civil aircraft. However, reducing airframe noise often leads to aerodynamic perfor-mance loss in the meantime. In this case, an approach based on artificial neural network is intro-duced. An established database serves as a basis and the training sample of a back propagation (BP) artificial neural network, which uses confidence coefficient reasoning method for optimization later on. Then the most satisfactory configuration is selected for validating computations through the trained BP network. On the basis of the artificial neural network approach, an optimization pro-cess of slat cove filler (SCF) for high lift devices (HLD) on the Trap Wing is presented. Aerody-namic performance of both the baseline and optimized configurations is investigated through unsteady detached eddy simulations (DES), and a hybrid method, which combines unsteady DES method with acoustic analogy theory, is employed to validate the noise reduction effect. The numerical results indicate not merely a significant airframe noise reduction effect but also excel-lent aerodynamic performance retention simultaneously.     

用神经网络法编程加工轮毂上特殊曲面的探讨

根据轮毂上曲面的特殊性,提出了采用神经网络方法进行编程,实现数控加工,以取代传统的加工方法的设想。     

人工神经元网络在变压器分接头实时控制中的应用

本文提出了一种利用人工神经元网络实时控制变压器分接头的新方法。研究结果表明该方法具有快速准确等优点，适应电力系统高度自动化的要求，并为变压器分接头实时控制开辟了一条新途径。