人工神经网络在电力系统短期负荷预测中的应用

根据电力系统短期负荷变化的特性,提出BP模型在实际负荷预测应用中的方法和步骤．对BP网络结构、样本空间、收敛性等作了有针对性的研究．结果表明：多层神经网络应用于电力系统短期负荷预测是可行和有效的．其预报结果比传统的负荷预测方法更准确、经济、效果更好．     

一种基于安全隧道技术的SSL VPN及其性能分析

根据VPN的定义,分析了一种基于隧道技术的SSLVPN的构成原理、系统结构、应用模型和其安全性,以及其相对于IPSecVPN的优越性,并通过实验测定了其性能参数。     

Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to ‘blow the whistle’ in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two‐variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981–1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33–58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model. Copyright © 2000 John Wiley & Sons, Ltd.     

Neural networks and the multinomial logit for brand choice modelling: a hybrid approach

The study of brand choice decisions with multiple alternatives has been successfully modelled for more than a decade using the Multinomial Logit model. Recently, neural network modelling has received increasing attention and has been applied to an array of marketing problems such as market response or segmentation. We show that a Feedforward Neural Network with Softmax output units and shared weights can be viewed as a generalization of the Multinomial Logit model. The main difference between the two approaches lies in the ability of neural networks to model non‐linear preferences with few (if any) a priori assumptions about the nature of the underlying utility function, while the Multinomial Logit can suffer from a specification bias. Being complementary, these approaches are combined into a single framework. The neural network is used as a diagnostic and specification tool for the Logit model, which will provide interpretable coefficients and significance statistics. The method is illustrated on an artificial dataset where the market is heterogeneous. We then apply the approach to panel scanner data of purchase records, using the Logit to analyse the non‐linearities detected by the neural network. Copyright © 2000 John Wiley & Sons, Ltd.     

基于语义特征提取与层次结构的问题生成方法

针对传统端到端模型在输入文本语义较复杂情况下生成的问题普遍存在语义不完整的情形,提出一种基于语义特征提取的文本编码器架构.首先构建双向长短时记忆网络获得基础的上下文信息,然后采用自注意力机制及双向卷积神经网络模型分别提取语义的全局特征和局部特征,最后设计一种层次结构,融合特征及输入自身信息得到最终的文本表示进行问题生成.在数据集SQuAD上的实验结果表明,基于语义特征提取与层次结构进行问题生成效果显著,结果明显优于已有方法,并且语义特征提取和层次结构在任务的各评价指标上均有提升.     

基于BQPSO的无线传感器网络节点调度算法

在多目标跟踪中,要求无线传感器网络在满足跟踪精度的前提下,最大限度地降低对传感器资源的使用。基于这一目的,适当选择节点避免共线度过高,并采用APIT实现精确定位,同时考虑跟踪簇总能耗设计节点调度目标函数,采用二进制量子粒子群优化算法解决传感器资源冲突问题。仿真结果表明:虽然基于BQPSO的节点调度算法比基于PSO的节点调度算法在能耗上增加了17.47%,但定位精度可以提高31.84%。算法在提高定位精度的同时最大限度地降低了对资源的使用,有效延长了无线传感器网络的工作寿命。     

基于LSTM神经网络的乘用车能耗预测

为改善当今石油供需矛盾和环境问题,针对乘用车提出了基于LSTM神经网络的燃油乘用车能耗预测模型。通过纵向动力学建模并匹配相应车型进行求解,结合GB/T 38146.1行驶工况数据,得出能耗随时间的变化率。构建LSTM神经网络架构,根据处理后的数据样本,对LSTM神经网络进行训练和评价。最后,通过LSTM神经网络和BP神经网络的仿真对比表明,随着迭代周期的增加,LSTM神经网络模型具有更高的精度,对能耗预测的准确性较好,对改善无人驾驶车辆的节能减排具有工程应用价值。     

基于Multi-Agent技术的三层信息融合系统研究

针对传统传感器网络管理复杂,系统信息融合智能化不高、精度低和模式单一、结构不清晰等不足,首先分析了Multi-Agent技术、传感器网络技术以及信息融合技术的独特优势,然后采用计算机网络分层结构思想和基于人工智能本体的知识表达理念,在信息融合过程中采用改进的SVM分类方法,构建了一种基于Multi-Agent技术的多传感器三层信息融合系统并对其具体融合过程进行了分析。最后对分类过程用MATLAB进行了分析。实验结果表明:系统分类精度较高,一定程度上不仅明显弥补了传统传感器的诸多不足,而且为后期决策提供了较为精准的目标参数。     

基于载荷均衡的韧性配电网分布式故障恢复

韧性电力系统旨在应对电力系统的极端事件,高效的故障恢复是提高配电网韧性的重要手段。为了提升配电网的韧性,提出了基于载荷均衡的分布式故障恢复方法。首先,相邻馈线终端单元根据故障恢复区负荷、相邻馈线容量及负载率,计算各相邻馈线完成故障恢复后的负载率期望值。然后,各相邻馈线终端单元均以该期望值为目标,分别完成各自的故障恢复路径。最后,根据各相邻馈线的故障恢复路径形成故障恢复区的故障恢复方案。算例分析结果表明,该方法在保证各馈线载荷均衡的前提下可以有效而快速地实现故障恢复。     

一种3GPP LTE系统快速上行切换方案

针对3GPP长期演进(LTE)移动通信系统,提出一种上行链路的快速切换方案。该方案充分利用了多个基站提供的选择性分集增益,同时避免了乒乓效应,可以提高上行链路的性能。