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。     

基于贯序正则极端学习机的时间序列预测及其应用

为实现对液压泵特征参数的在线预测,提出一种贯序正则极端学习机(SRELM),并研究了基于SRELM的预测方法.SRELM根据结构风险最小化原理实现网络训练,其网络权值可随新样本的逐次加入而递推求解,具有泛化能力强与训练速度快的优点,因此适于特征参数的在线预测.基于SRELM的预测方法利用特征参数训练SRELM模型,以逐...     

New Neural Network Response Surface Methods for Reliability Analysis

This article presents two new kinds of artificial neural network (ANN) response surface methods (RSMs): the ANN RSM based on early stopping technique (ANNRSM-1), and the ANN RSM based on regularization theory (ANNRSM-2). The following improvements are made to the conventional ANN RSM (ANNRSM-0): 1) by monitoring the validation error during the training process, ANNRSM-1 determines the early stopping point and the training stopping point, and the weight vector at the early stopping point, which corresponds to the ANN model with the optimal generalization, is finally returned as the training result; 2) according to the regularization theory, ANNRSM-2 modifies the conventional training performance function by adding to it the sum of squares of the network weights, so the network weights are forced to have smaller values while the training error decreases. Tests show that the performance of ANN RSM becomes much better due to the above-mentioned improvements: first, ANNRSM-1 and ANNRSM-2 approximate to the limit state function (LSF) more accurately than ANNRSM-0; second, the estimated failure probabilities given by ANNRSM-1 and ANNRSM-2 have smaller errors than that obtained by ANNRSM-0; third, compared with ANNRSM-0, ANNRSM-1 and ANNRSM-2 require much fewer data samples to achieve stable failure probability results.     

Cooperative Routing for Maximizing Network Performance of Wireless Networks

To greatly increase spectral efficiency and improve network performance in wireless networks,a novel cooperative routing algorithm,namely maximum throughput cooperative routing(MTCR) algorithm,is proposed.According to cooperative link model,throughput analysis is presented to evaluate performance improvement in the process of exploiting cooperative commu-nication from physical layer to higher layer.Taking the throughput improvement as performance metric,a cooperative relay se-lection scheme is developed.Finally,based on the route constructed by adaptive forwarding cluster routing(AFCR) algorithm,each node on the route selects the optimum cooperative node from all the potential cooperative nodes to construct the coopera-tive link with maximum throughput so that cooperative route with maximum network throughput from source to destination can be set up.Simulation results show that compared with the noncooperative routing algorithm and minimum power selection de-code-and-forward(MPSDF) routing algorithm,the proposed algorithm can obviously improve network throughput in the pres-ence of low transmission power,large number of nodes and high spectral efficiency.     

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     

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。     

一个拼音汉字自动转换系统的设计与实现

给出了拼音汉字转换的定义，并建立了一个带调拼音词句到汉字语句的自动转换系统。它由五个部分组成，自动分词模块，词法分析模块，句法和语义分析模块，语义修饰模块和显示模块。其中句法分析是系统的核心，在比较各种方法的基础上选择了扩充转移网络模型，使ＡＴＮ的优点和音字转换的特点得到较好的结合，测试结果表明，系统的正确转换率达到９７％。     

计算机会议系统信息传播模型的研究与分析

首先简单论述了计算机会议系统着急的为通信问题，然后详细分析了多点信息传播的集中式、层次式和分布式三种方法，并给出各自时间复杂性分析的数学推导和计算机仿真结果。     

基于神经网络的轴承故障预测模型

利用在故障预测领域广泛应用的神经网络模型,对轴承监测数据的特征提取与建模,挖掘出监测数据与剩余寿命间内在关联,从而对轴承剩余寿命做出评估。在轴承全寿命数据的实际实验中,证实了该模型的有效性。     

Forecasting the geomagnetic activity of the Dst index using multiscale radial basis function networks

The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models from limited input–output observational data, provides a powerful tool for analysing and forecasting geomagnetic activities including the prediction of the Dst index. In this study, the process of the Dst index is treated to be a structure-unknown system, where the solar wind parameter (VBs) and the solar wind dynamic pressure (P) are the system inputs, and the Dst index is the system output. A novel multiscale RBF (MSRBF) network is introduced to represent such a two-input and single-output system, where the Dst index is related to the solar wind parameter and the dynamic pressure, via a hybrid network model consisting of two submodels: a linear part that reflects the linear relationship between the output and the inputs, and a nonlinear part that captures the effect of the interacting contribution of past observations of the inputs and the output, on the current output. The proposed MSRBF network can easily be converted into a linear-in-the-parameters form and the training of the linear network model can easily be implemented using a forward orthogonal regression (FOR) algorithm. One advantage of the new MSRBF network, compared with traditional single scale RBF networks, is that the new network is more flexible for describing complex nonlinear dynamical systems.