大数据浪潮冲击下网络科学与工程面临的挑战与机遇

近年在大数据浪潮的冲击下,“网络科学与工程”与众多领域一样,面临着新的挑战和机遇。首先评述大数据及大数据科学的特点和概况;然后针对当今“网络科学与工程”的最新前沿课题--“网络的网络(NON)”,讨论若干典型的NON 及其主要特点和相关课题初步进展,并提供若干挑战性课题和难得的机遇。     

Metric inference for social networks

Using a natural metric on the space of networks, we define a probability measure for network-valued random variables. This measure is indexed by two parameters, which are interpretable as a location parameter and a dispersion parameter. From this structure, one can develop maximum likelihood estimates, hypothesis tests and confidence regions, all in the context of independent and identically distributed networks. The value of this perspective is illustrated through application to portions of the friedship cognitive social structure data gathered by Krackhardt (1987).We thank Ove Frank, David Krackhardt, the editor and the referees for their constructive comments and suggestions.     

The RCT approach for cell coverage prediction (II): Experiment and early results

We study on the implementation flow of the radio computerized tomography (RCT) prediction method. A case in real cellular mobile radio (CMR) system together with the prediction results are also presented. As shown by the results, the RCT prediction method is marked for its convenience and rapidity, as well as its relative high precision even when the prediction procedure is highly simplified. Since it is developed according to the characteristics of wireless communication environments of our country and has concurrently merits from both statistical and deterministic prediction models, the RCT prediction method is in good agreement with engineering practices in cellular mobile communication in cities at home. Optimized by combining with other techniques, further improvement could be achieved in the stability and precision of the RCT prediction method which now serves as the core part of a software tool for commercial use in CMR system analysis and optimization. Biography: Yu Sheng-bing (1974-), male, Ph. D candidate, research interest: theory analysis and application development of radio propagation in wireless communication systems.     

An ensemble of LSTM neural networks for high‐frequency stock market classification

We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large‐cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles.     

基于深度贝叶斯网络学习的不确定性建模方法

战场态势评估涉及很多不确定因素,对不确定性进行仿真建模能够提高态势评估的能力。针对参战对象多元、不确定性增多导致的无法全面准确表达不确定性问题,提出了基于记忆模块和变分自编码器的深度贝叶斯网络模型。采用生成模型设计了基于深度贝叶斯网络学习的态势评估模型;阐述了融合记忆模块的深度生成模型原理和模型的学习与推理过程;以某空袭行动为例构建贝叶斯网络,对所提方法进行了验证。结果表明:深度神经网络能够逼近隐变量的非线性变换,设计的记忆模块能存储深度神经网络提取的大量局部特征,通过学习自动得到了贝叶斯网络条件概率,增强了不确定性建模能力。     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

基于MDA机制的跨洋航空网时隙分配方法

考虑到飞机跨洋飞行的限制性条件,提出了一种改进的多单元双向拍卖机制来满足跨洋航空主干网的时隙分配的需要。该机制将VCG双向拍卖和传统的多单元双向拍卖进行结合和改进,并从激励相容、预算平衡、个人理性和机制效率等方面证明了该双向拍卖机制的性质。最后的仿真结果表明,该双向拍卖机制具有较高的效率。     

A Neuro‐wavelet Model for the Short‐Term Forecasting of High‐Frequency Time Series of Stock Returns

We propose a wavelet neural network (neuro‐wavelet) model for the short‐term forecast of stock returns from high‐frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non‐stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non‐decimated wavelet‐based multi‐resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one‐, three‐ and five step‐ahead horizons was achieved by the proposed model. The procedure used to build the neuro‐wavelet model is reusable and can be applied to any high‐frequency financial series to specify the model characteristics associated with that particular series. Copyright © 2013 John Wiley & Sons, Ltd.     

具有混合执行器故障的多智能体分布式有限时间自适应协同容错控制

针对多智能体编队系统执行器发生故障时,所引起的参数不确定以及系统瞬态不稳定问题,本文采用径向基函数神经网络(radial basis function neural networks, RBFNNs)对不确定参数(未知函数)进行估计。同时,基于反推技术设计出合理的自适应容错控制器,并通过有限时间理论保证系统实现瞬态稳定。首先,本文采用10个智能体作为被控对象,基于有向通讯拓扑结构理论,构建了非线性多智能体系统模型。其次,基于RBFNNs逼近特性,采用反推技术与动态面技术相结合,设计出合理的容错控制器,补偿多智能体中出现的未知非线性执行器故障,并采用有限时间理论解决系统瞬态不稳定问题。接着,基于Lyapunov稳定性理论分析了控制器的稳定性和快速收敛性。最后,通过两种算例对比,验证了所设计的控制器性能优于传统的反推技术,为工程实践提供了一种有效的研究思路。     

基于元胞自动机的企业生产过程的研究

为减少和预防企业生产中意外事故的发生,将元胞自动机理论应用于企业生产过程的研究,建立了某企业生产中基层-员工执行系统的元胞自动机模型.通过对安全生产的个人因素和环境因素在不同状态下的模拟和分析,认为员工遵守规章规程的程度是影响企业安全生产的关键因素之一;并在此基础上,建议企业多关注于如何保证政策法规被规范执行,以缩小实际操作与规则规程之间的差距.这为企业安全生产的研究提供了一种新的思路.