Stock Price Forecasting: An Echo State Network Approach

Forecasting stock prices using deep learning models suffers from problems such as low accuracy, slow convergence, and complex network structures. This study developed an echo state network (ESN) model to mitigate such problems. We compared our ESN with a long short-term memory (LSTM) network by forecasting the stock data of Kweichow Moutai, a leading enterprise in China’s liquor industry. By analyzing data for 120, 240, and 300 days, we generated forecast data for the next 40, 80, and 100 days, respectively, using both ESN and LSTM. In terms of accuracy, ESN had the unique advantage of capturing nonlinear data. Mean absolute error (MAE) was used to present the accuracy results. The MAEs of the data forecast by ESN were 0.024, 0.024, and 0.025, which were, respectively, 0.065, 0.007, and 0.009 less than those of LSTM. In terms of convergence, ESN has a reservoir state-space structure, which makes it perform faster than other models. Root-mean-square error (RMSE) was used to present the convergence time. In our experiment, the RMSEs of ESN were 0.22, 0.27, and 0.26, which were, respectively, 0.08, 0.01, and 0.12 less than those of LSTM. In terms of network structure, ESN consists only of input, reservoir, and output spaces, making it a much simpler model than the others. The proposed ESN was found to be an effective model that, compared to others, converges faster, forecasts more accurately, and builds time-series analyses more easily.     

Double Medical Images Zero-Watermarking Algorithm Based on the Chaotic System and Ternary Accurate Polar Complex Exponential Transform

Journal of Mathematical Imaging and Vision - Different from the traditional watermarking schemes, zero-watermarking schemes are lossless embedding methods, which are applicable to be used in...     

Sensitive attribute privacy preservation of trajectory data publishing based on l-diversity

Distributed and Parallel Databases - The widely application of positioning technology has made collecting the movement of people feasible for knowledge-based decision. Data in its original form...     

2011年12月计算机病毒疫情分析

根据国家计算机病毒应急处理中心对互联网的监测,2011年12月计算机病毒整体感染情况呈平稳态势,主要传播途径仍以＂网络钓鱼＂和＂网页挂马＂为主。     

莱钢宽厚板MES的开发与应用

为了满足宽厚板生产小批量、多品种、多工序、多要求的实际情况,结合工艺要求和先进的计算机技术开发出一套铸轧一体化的MES系统,使得各种生产数据及特殊生产要求准确无误并及时发送到现场的执行系统,兼顾了质量与效率;组板、组炉、组浇模型的采用为大规模生产创造了条件,兼顾了成本与效率.精确到每块材料的合同跟踪模型保证了产品的如期交货,从而保证了较高的客户满意度.系统采用成熟的三层架构,数据库采用稳定的Oracle数据库技术,前台开发工具采用主流的VS2008,数据库和应用服务器均采用IBM的Aix操作系统,保证了系统的稳定、高效、安全.通过MES的实施较好地解决了制造环节的各种问题.     

无线机电阻抗结构健康监测系统软件开发

机电阻抗技术是一种新兴的非参数结构健康监测方法,对结构局部微缺陷比较敏感;为了实现基于机电阻抗技术的结构健康监测,采用Visual C++和Matlab混合编程的方式设计和开发了系统监测软件;该软件用于控制现场的无线智能机电阻抗传感器进行数据采集,同时对测量数据进行处理,根据损伤评估算法评定结构健康状况;软件采用Visual C++编写系统人机交互界面和通信接口程序,采用Matlab编写数据处理和损伤评估算法,同时图形化输出运算结果;将两者的优势有机结合,从而缩短了软件的开发周期。     

攻击型无人机切换控制系统容错算法研究

攻击型无人机单侧发射或投放重型武器时,可将飞行控制系统看作是一个切换控制系统。针对常见的常值型和时变型两种执行器故障,提出了一种自适应观测器设计方法,同时结合状态反馈控制设计了容错控制器。通过Lyapunov函数推出了观测器的误差是关于故障界输入状态实际稳定的充分条件,通过平均驻留时间方法证明了切换控制系统是全局输入状态实际稳定的。某型攻击型无人机仿真实例表明,此方法能在准确估计故障大小的基础上,有效保持切换飞行控制系统的性能,应用前景十分广阔。     

NoSQL非关系型数据库负载均衡的实现

NoSQL数据库以其支持数据高并发读写,海量数据高效率存储和访问,以及高扩展性和高可用行的特点在分布式存储系统中得到了广泛的应用。通过对分布式存储系统负载均衡的研究,提出了使用一致性哈希函数来实现系统负载均衡,并通过添加为集群节点添加虚拟节点的方式来增加缓存的命中率。     

基于MeeGo的电容式触摸屏驱动设计

随着人们对操控要求的不断提高,电容触摸屏因为能支持多点触摸而得到广泛使用。本文基于Nokia和Intel公司合作开发的开源操作系统MeeGo,采用基于内核对象的Linux输入子系统来设计触摸屏的驱动。该方案极大地方便了触摸屏的驱动开发,可应用在车载娱乐、上网本、智能手机等电子产品上。     

双发模式下视三维SAR成像方法研究

在合成孔径雷达(SAR)成像优化过程中,由于受到地形环境噪声的影响,使成像模糊和信息丢失,利用阵列天线SAR(LA-SAR)进行成像是一个不错的解决方法。然而传统的单发模式要求有足够多的阵元以满足空间采样率的要求,工程实现的难度较大。为解决上述问题,提出了一种采用正负斜率线性调频信号的双发模式下视三维SAR成像新方法,可以大幅减少天线阵元的数目,提高雷达平台的空间利用率,有效降低工程实现的难度。分析校正了俯仰向的距离徙动因子,给出了双发模式下回波信号的处理方法。进行仿真的结果证明了改进的三维成像方法对成像优化的有效性。