面向互联网的信息处理

随着互联网海量信息的快速增长，由原来单一的以文本信息为主的信息处理发展成文本、语音、图像等多模态的信息处理．同时，用户的需求也从关键词搜索为主的信息获取向着基于语义理解的自动问答、辅助决策等智能交互的方向发展．本文从互联网服务信息处理特点以及用户需求变化出发，阐述了互联网信息处理面临的挑战和发展趋势．首先介绍了基于互联网的海量信息处理特点以及基本方法，然后分别阐述了互联网文本信息处理、语音信息处理、图像信息处理、位置信息处理的挑战以及发展趋势．最后介绍了如何对互联网的用户从属性、状态、兴趣3个维度进行建模，以满足用户个性化服务和商业分析的需要．     

中国计算机产业的下一个亮点--汉语语音合成的实用化

现代社会已经进入数字化信息时代，网络技术和多媒体技术获得迅猛发展，计算机与人之间的互日益频繁，如何使电脑具有类似于人一样的听，说能力，成为自90年代以以来信息产业的研究热点，要建立一个具有听、说能力的计算机语音系统、必需的两项关键技术就是语音识别技术与语音合成技术。同语音识别技术相比， 语音合成技术相对成熟一些，是该领域中近年期最有希望产生突破性进展并形成产业化的技术，而汉语语音合成的实用化更将成为中国计算机产业的一个亮点，近几十年来国际和国内对于语音合成技术的研究主要集中在按规则进行文语转换，即将书面语言转换成口头语言，到目前为止，法语、德语、英语、日语等语种的文语转换系统都已经研制成功，相对而言，中文语音合成技术现在还尚未达到实用化的要求，本文对当前语音合成中热点的文本分析，韵律生成，语音合成三项关键技术进行了剖析，并针对中文的文语特点，指出了中文语音合成技术的难点所在，勿庸置疑，中文语音合成具有非常惊人的调潜力，因而必将成为国内外IT业争夺的重点，虽然国内的语音事成技术起步较晚，但是我们拥有其他非汉语言国家所不同能相比的优势，汉语对于我们来说是如此熟悉，以至于我们可以说：我们拥有一支完全可以驾驶汉字及其语音处理技术的人才队伍，而且我们已经在汉字输入、输出、汉字排版，汉字OCR方面取得了举世瞩目的成绩，因此，真正实用的汉语语音事成系统应该，也必交在我国本土研究成功。     

医用血管支架非线性扩张过程快速预测方法的研究

建立了血管支架变形影响因子与其变形结果之间的具有高度非线性识别能力的神经网络模型，通过引入学习因子η和动量因子ψ,采用附加动量项的权值修正方法，优化了网络训练算法，从而提高了网络训练速度和系统鲁棒性．结合实例对网络进行训练，并对预测误差进行了统计假设检验，检验结果表明血管支架变形神经网络智能预测结果与非线性有限元分析结果误差均值低于0．03％，训练后的网络能够较好地对血管支架变形进行预测．在此基础上，基于Pro／Toolkit工具，融合血管支架扩张变形神经网络智能预测模型，建立了血管支架力学性能快速评价工具，该系统实用性强、效率高，能大幅缩短血管支架产品开发周期     

改进的模糊神经网络算法在车型识别中的应用

FBP和FCNN网络是模式识别中应用最为广泛的两种神经网络，本文将这两种网络应用于车型识别，分别建立了车型识别模型。利用混沌对初值的极端敏感依赖提出了FCNN网络算法，通过对车型图像数据库进行仿真实验，对比分析它们各自的识别率和泛化能力等性能指标，证明了FCNN网络算法的有效性。     

编者按

信号与信息处理是信息科学与技术的一个十分重要的部分，只有通过信号与信息处理才能将从传感器中获得的信号与信息变成我们所需的结果．与很多科学领域不同，信号与信息处理一方面有着本身的基础问题研究，而另一方面又有在其他科学领域的应用问题．正是这两者美妙的结合，才使得信号与信息处理在过去20年里有了蓬勃辉煌的发展，各类新理论、新技术与新应用层出不穷．     

一种结构自适应的聚类神经网络

为了解决聚类分析中聚类数的确定问题，在SOFM神经网络的基础上，从聚类准则出发，通过试验对聚类准则的曲线特征进行了详细的分析和论证，设计出一种结构自适应的聚类神经网络，该网络能自动确定最佳的聚类数，并提出了一种减少计算量的改进算法。     

单张正面人脸图像的三维人脸重建方法研究

针对基于单张正面人脸图像进行三维人脸重建时所需脸部侧面深度信息缺失的问题，提出基于BP神经网络快速三维重建方法。通过建立BP神经网络估计出正侧面人脸数据的关系，从而由输入的正面数据得到侧面数据，并对BP算法做出改进，加速了算法的收敛，提高了拟合的精度。然后利用获取的人脸侧面数据调整CANDIDE-3人脸模型，生成近似图...     

改进果蝇优化算法优化广义回归神经网络的预测问题研究

为提高传统非线性预测模型的预测精度,提出一种基于改进果蝇优化算法优化广义回归神经网络的预测方法,将果蝇群体分两部分分别进行迭代寻优,从而改进了果蝇优化算法的寻优性能,进而避免了在寻优过程中陷入局部最优。该方法利用改进果蝇优化算法优化广义回归神经网络的径向基函数扩展参数,然后用训练好的广义回归神经网络预测模型进行预测,最后通过订单预测算例进行实证研究。实证研究结果显示,该方法在解决订单预测问题中与未改进的果蝇优化算法优化广义回归神经网络和传统的广义回归神经网络方法对比,具有更高的预测精度和更好的非线性拟合能力。     

信息系统、信号信息处理发展基础性思考

在人类信息社会发展中，信息科技支持各类信息系统发展，同时也是服务人类不可或缺的工具，信息系统服务人类具体体现在其分功能集成的程度不断提高，并关联到众多学科，信号信息处理是其中的重要组成部分．本文由普适的系统理论为起点，多层次扼要地讨论了人发挥主动性不断掌握信息科技要点，并对信息科技及系统如何实施进化机理的进化，从而争取超越式发展进行了思考和论述．     

基于随机时间自动机和统计模型检验技术的无线传感网络协议建模与分析

近年来，传感器技术得到了长足而有效的提升，无线传感网络（WSN）以其开放、动态的特征获得了极大的关注，并成为了互联网计算的一个重要组成．WSN系统行为复杂，经常面临信息丢失、节点动态变化等不确定因素，且网络中的节点一旦部署将很难更改、维护．因此，为了保证相关应用的正常工作，在系统设计阶段对WSN中的底层协议进行质量保障就成为了一项非常重要的研究问题．系统设计人员不仅需要保证协议功能上的正确性，还应该评估协议在目标工作环境下的性能，以保证其可以胜任相应的工作需求．针对以上问题，本文提出了一种基于随机时间自动机和统计模型检验技术的WSN协议建模、分析和评估途径．在建模阶段，首先将采用时间自动机对协议在理想环境下的基本业务流程进行建模．考虑到WSN系统实际工作中会遇到的各种不确定性因素，将用带权分枝来对模型进行扩展，生成协议的随机时间自动机．在验证阶段，首先采用经典模型检验技术，在理想时间自动机上检验相关功能性质，保证协议工作逻辑的正确性．为评估协议在不同条件下的具体性能，则在随机时间自动机上用统计模型检验技术对其进行数值分析，以进行参数配置、性能预测、协议比较等工作．为展示该途径的可用性及其技术细节，本文对两种著名的WSN时间同步协议，TPSN和FTSP分别进行了完整的建模与评估．     

An intelligent model selection and forecasting system

In this paper we present an intelligent decision‐support system based on neural network technology for model selection and forecasting. While most of the literature on the application of neural networks in forecasting addresses the use of neural network technology as an alternative forecasting tool, limited research has focused on its use for selection of forecasting methods based on time‐series characteristics. In this research, a neural network‐based decision support system is presented as a method for forecast model selection. The neural network approach provides a framework for directly incorporating time‐series characteristics into the model‐selection phase. Using a neural network, a forecasting group is initially selected for a given data set, based on a set of time‐series characteristics. Then, using an additional neural network, a specific forecasting method is selected from a pool of three candidate methods. The results of training and testing of the networks are presented along with conclusions. Copyright © 1999 John Wiley & Sons, Ltd.     

基于STDP规则和忆阻桥突触的神经网络及图像处理

忆阻器是具有记忆和类突触特性的非线性电路元件.基于此特性,文中提出了一个基于STDP（spike-time-dependent plasticity）学习规则的忆阻桥突触电路,它具有可以作为人工神经网络突触的优势.根据此优势,将这个新的电路与其他电路和网络结合,构成全新的电路和网络.首先将该忆阻桥突触电路和3个附加的晶体管结合在一起,实现神经网络的突触运算,并构建完整的忆阻桥突触神经网络.然后再将它与细胞神经网络结合用于图像去噪、边缘提取、角检测和汉字识别.最后,通过一系列的仿真实验证实了该方案的可行性,说明基于STDP学习规则的忆阻桥突触神经网络更具仿生特性,而且集成度更高、模板更易更换,有望解决实时的复杂的智能问题.     

Performance evaluation of neural network architectures: the case of predicting foreign exchange correlations

In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. It is apparent that a better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real‐world problems. Unlike many existing studies which focus on a single type of network architecture, this study evaluates and compares the performance of models based on two competing neural network architectures, the multi‐layered feedforward neural network (MLFN) and general regression neural network (GRNN). Our empirical evaluation measures the network models' strength on the prediction of currency exchange correlation with respect to a variety of statistical tests including RMSE, MAE, U statistic, Theil's decomposition test, Henriksson–Merton market timing test and Fair–Shiller informational content test. Results of experiments suggest that the selection of proper architectural design may contribute directly to the success in neural network forecasting. In addition, market timing tests indicate that both MLFN and GRNN models have economically significant values in predicting the exchange rate correlation. On the other hand, informational content tests discover that the neural network models based on different architectures capture useful information not found in each other and the information sets captured by the two network designs are independent of one another. An auxiliary experiment is developed and confirms the possible synergetic effect from combining forecasts made by the two different network architectures and from incorporating information from an implied correlation model into the neural network forecasts. Implied correlation and random walk models are also included in our empirical experiment for benchmark comparison. Copyright © 2005 John Wiley & Sons, Ltd.     

Modeling of frequency containment reserve prices with econometrics and artificial intelligence

The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one-step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.     

In vitro neurogenesis: development and functional implications of iPSC technology

Neurogenesis is the developmental process regulating cell proliferation of neural stem cells, determining their differentiation into glial and neuronal cells, and orchestrating their organization into finely regulated functional networks. Can this complex process be recapitulated in vitro using induced pluripotent stem cell (iPSC) technology? Can neurodevelopmental and neurodegenerative diseases be modeled using iPSCs? What is the potential of iPSC technology in neurobiology? What are the recent advances in the field of neurological diseases? Since the applications of iPSCs in neurobiology are based on the capacity to regulate in vitro differentiation of human iPSCs into different neuronal subtypes and glial cells, and the possibility of obtaining iPSC-derived neurons and glial cells is based on and hindered by our poor understanding of human embryonic development, we reviewed current knowledge on in vitro neural differentiation from a developmental and cellular biology perspective. We highlight the importance to further advance our understanding on the mechanisms controlling in vivo neurogenesis in order to efficiently guide neurogenesis in vitro for cell modeling and therapeutical applications of iPSCs technology.     

基于△DOR信号的高精度VLBI技术

在嫦娥二号（CE-2）工程中，我国首次开展了X波段ADOR测量实验，获取了ADOR信号的VLBI时延数据，并用于精密定轨．本文给出了CE-2中ADOR信号的VLBI测量与数据处理方法，结合我国VLBI测量系统对时延数据进行了误差分析及精密定轨分析．结果表明：ADOR信号的VLBI时延精度优于0．5ns，比利用S波段信标的测量精度提高约一个数量级．本研究成果为后续的月球及深空探测高精度测定轨提供了重要的技术手段．     

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.     

基于客户满意度的模糊神经网络JSP研究

针对具有不确定性因素的作业车间调度问题，基于模糊数学的思想，把模糊加工时间、间隔期和模糊交货期用梯形模糊数表示，建立了基于客户满意度曲模糊作业车间调模型。运用Hopfleld神经网络算法求解，结合目标函数和JSP的全部约束条件，构建能量函教和JSP换位矩阵，保证了神经网络稳态输出为最优生产调度方案。最后用网络计划图对稳态输出的换位矩阵进行解码得到最优调度甘特图，避免了传统成本树法易出现死锁调度的问题。计算实例验证了本算法的可行性和有效性。     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.