一种k均值和神经网络集成的语音识别方法

提出了一种基于k均值聚类和BP神经网络集成的语音识别方法,该方法以神经网络集成模型为基础,利用k均值聚类算法选择部分有差异性的个体神经网络再进行集成学习,既克服了单个BP网络模型容易局部收敛和不稳定性的缺点,又解决了传统集成方法训练时间长和个体网络差异性不明显的问题。通过对非特定人孤立词的语音识别的实验,证实了该方法的有效性。     

基于Bagging的概率神经网络集成分类算法

目前的神经网络较多集中在以BP算法为基础的BP神经网络上。针对BP神经网络的不足,在分析研究概率神经网络和机器学习的基础上,结合集成学习的思想,提出了基于Bagging的概率神经网络集成分类算法。理论分析和实验结果都表明,提出的算法能够有效地降低分类误差,提高分类准确率,具有较好的泛化能力以及较快的执行速度,能够取得比传统的BP神经网络分类方法更好和更稳定的分类结果。     

微铣削刀具磨损状态监测方法研究

为提高金属微铣削过程中刀具磨损状态在线监测系统的预测效率与精度,提出一种基于线性判别分析与改进型BP神经网络模型识别刀具磨损的方法;该方法通过传感器与数据采集系统采集微铣削过程振动信号,提取其时域和频域特征并通过线性判别方法进行降维约简;将降维后的特征输入经灰狼优化改进的BP神经网络模型,从而实现微铣刀磨损状态特征的分类;结果表明,提出的微铣刀在线监测方法能够准确识别微铣刀的各种磨损状态;此外,和其它分类算法相比,提出的基于灰狼优化算法的BP神经网络模型在分类精度和计算效率方面具有综合优势;这对实际生产过程中微铣刀的磨损状态监测具有非常重要的实际意义.     

基于神经网络集成的思维脑电信号分类方法

提出基于神经网络集成算法的思维脑电信号分类方法,采用BP神经网络为分类器,对用AR参数提取的思维脑电特征进行分类。为进一步提高BP神经网络的分类性能,采用Bagging算法对BP神经网络分类器进行加权投票,实验表明,提出的方法具有很好的分类效果。     

改进的BP神经网络在运动想象脑电分类研究中的应用

传统BP神经网络算法由于自身结构设置导致反馈调节能力不甚理想,在运动想象（MI）脑电信号多分类问题上的鲁棒性与识别性能提升上还有待进一步优化。本文研究并应用了基于粒子群算法（PSO）与蝙蝠算法（BA）的改进BP神经网络,经过初始编码序列寻优实现对原始BP神经网络的权值与阈值的更新,进而提升BP算法在MI脑电信号中的分类识别能力。对08年BCI竞赛数据MI脑电信号的包络幅值特征进行四分类,平均结果为96.25%;实验室采集的MI脑电数据作进一步验证,二分类准确率为89.25%。实验结果表明BA-BP模型分类精度相较于PSO-BP方法提高了约24%,迭代效率提升了约50%,一定程度上抑制BP神经网络算法局部最优的出现,为脑电信号的多分类识别问题解决提供了一种新的途径。     

量子遗传算法优化BP神经网络的网络流量预测

为了提高网络流量的预测精度,提出了一种改进的多种群量子遗传算法优化BP神经网络的网络流量预测模型。在确定了神经网络的结构后,采用多种群量子遗传算法对BP神经网络的初始权值和阈值进行优化。该模型利用K均值聚类算法将种群划分成若干子种群,多个子种群分别进化以保持种群的多样性。子种群间通过移民操作进行信息交互,减小了算法陷入局部最优的概率。同时采用一种自适应的量子旋转门调整策略加快算法的收敛速度。仿真结果表明,相较传统方法,该模型在网络流量预测方面具有收敛速度快、预测精度高的优点。     

基于BP神经网络和Bagging算法的入侵检测

提出基于Bagging算法集成BP神经网络的入侵检测方法。采用BP神经网络为分类器,以用户的网络连接行为为特征进行检测,为进一步提高BP神经网络的分类性能,采用Bagging算法对BP神经网络分类器进行加权投票。实验表明,提出的方法具有良好的检测性能。     

基于粗糙集和BP神经网络的文本分类研究

研究文本分类、提高文本检索效率问题,针对文本特征维数过高导致神经网络收敛速度慢、文本分类精度低的难题,结合粗糙集的属性约简和神经网络的文本分类优点,提出了一种粗糙集(RS)结合BP神经网络的文本自动分类算法(RS-BPNN).RS-BPNN首先应用粗糙集理论的属性约简对文本特征预处理,降低向量维数,然后把冗余的属性从决策表中删去,最后利用神经网络进行分类.并在MATLAB环境中进行了仿真实验,仿真结果表明,RS-BPNN方法的识别精度比传统的BP神经网络高4%左右,提高了文本分类的精度和检索效率.     

基于降维BP神经网络的高维数据分类研究

为确保高维数据的神经网络分类精度,提出了先降维后分类的方法。采用主成分分析（PCA）法实现高维数据的降维。通过分析传统BP算法,提出分两步来更新网络权值的扰动BP学习方法。采用MATLAB对降维分类算法的分类精度和误差收敛速度进行分析。仿真结果显示：先降维再采用扰动BP网络进行高维数据分类可大大提高数据的分类精度和训练速度。     

PSO-BP网络模型在数据分类中的应用

分类技术是数据挖掘的重要分支,常见的分类方法有决策树、统计方法、机器学习方法、BP神经网络方法等.本文针对标准BP网络存在的一些缺陷,结合一种进化算法微粒群(PSO)算法,建立了一种用于数据分类的网络模型.该模型充分利用微粒群算法的全局寻优特性,优化BP网络的权值和阈值,既保证了BP网络能收敛到全局最优解,加快了BP网络的收敛速度和收敛精度,又提高了待分类数据的识别准确率.仿真实验结果表明此模型较BP网络具有较好的分类识别性能.     

煤矿井下安全监控分站的设计及其仿真实现

为了设计一种具有低成本、低功耗、易操作、功能强且可靠性高的煤矿井下安全分站,针对煤矿安全生产实际,文章提出了采用MCS-51系列单片机为核心、具有CAN总线通信接口的煤矿井下安全监控分站的设计方案;首先给出煤矿井下安全监控分站的整体构架设计,然后着重阐述模拟量输入信号处理系统的设计过程,最后说明单片机最小系统及其键盘、显示、报警、通信等各个组成部分的设计;为验证设计方案的可行性与有效性,使用Proteus软件对设计内容进行仿真验证,设计的煤矿井下安全监控分站具有瓦斯、温度等模拟量参数超标报警功能和电机开停、风门开闭等开关量指示功能;仿真结果表明:设计的煤矿井下安全监控分站具有一定的实际应用价值.     

Call for Papers The 6^th International Conference on Grid and Cooperative Computing

Recent years have seen rapid advances in various grid-related technologies, middleware, and applications. The GCC conference has become one of the largest scientific events worldwide in grid and cooperative computing. The 6th international conference on grid and cooperative computing (GCC2007) Sponsored by China Computer Federation (CCF),Institute of Computing Technology, Chinese Academy of Sciences (ICT) and Xinjiang University ,and in Cooperation with IEEE Computer Soceity ,is to be held from August 16 to 18, 2007 in Urumchi, Xinjiang, China.     

View-based model-driven architecture for enhancing maintainability of data access services

In modern service-oriented architectures, database access is done by a special type of services, the so-called data access services (DAS). Though, particularly in data-intensive applications, using and developing DAS are very common today, the link between the DAS and their implementation, e.g. a layer of data access objects (DAOs) encapsulating the database queries, still is not sufficiently elaborated, yet. As a result, as the number of DAS grows, finding the desired DAS for reuse and/or associated documentation can become an impossible task. In this paper we focus on bridging this gap between the DAS and their implementation by presenting a view-based, model-driven data access architecture (VMDA) managing models of the DAS, DAOs and database queries in a queryable manner. Our models support tailored views of different stakeholders and are scalable with all types of DAS implementations. In this paper we show that our view-based and model driven architecture approach can enhance software development productivity and maintainability by improving DAS documentation. Moreover, our VMDA opens a wide range of applications such as evaluating DAS usage for DAS performance optimization. Furthermore, we provide tool support and illustrate the applicability of our VMDA in a large-scale case study. Finally, we quantitatively prove that our approach performs with acceptable response times.     

A pragmatic multi-method investigation of discrepant technological events: Coping,attributions, and ‘accidental’ learning

Discrepant technological events or situations that entail a problem, a misunderstanding or a difficulty with the Information Technology (IT) being employed, are common in the workplace, and can lead to frustration and avoidance behaviors. Little is known, however, about how individuals cope with these events. This paper examines these events by using a multi-method pragmatic approach informed by coping theory. The results of two studies – a critical incident study and an experiment – serve to build and test, respectively, a theoretical model that posits that individuals use a variety of strategies when dealing with these events: they experience negative emotions, make external attributions, and adopt engagement coping strategies directed at solving the event, eventually switching to a disengagement coping strategy when they feel they have no control over the situation. Furthermore, users’ efforts may result in ‘accidental’ learning as they try to overcome the discrepant IT events through engagement coping. The paper ends with a discussion of the results in light of existing literature, future opportunities for research, and implications for practice.     

The Distributed Time-Triggered Simulation Scheme: Core Principles and Supporting Execution Engine

Distributed real-time simulation is a young technology field but its practice is under increasing demands. In recent years the author and his collaborators have been establishing a new approach called the distributed time-triggered simulation (DTS) scheme which is conceptually simple and easy to use but widely applicable. The concept was initiated in the course of developing a new-generation object-oriented real-time programming scheme called the time-triggered message-triggered object (TMO) programming scheme. Some fundamental issues inherent in distributed real-time simulation that were learned during recent experimental studies are discussed along with some approaches for resolving the issues. An execution engine developed to support both the TMOs engaged in control computation and the TMOs engaged in DTS is also discussed along with its possible extensions that will enable significantly larger-scale DTSs.     

Software-Konfigurationsmanagement in großen Softwareprojekten

Zusammenfassung Mit zunehmender Größe der Softwaresysteme verschärfen sich die für die Software-Herstellung typischen Probleme: Beherrschen großer Objektmengen, Erhalten der Systemkonsistenz, Kontrolle der ständigen Änderungseinflüsse und Gewährleisten einer langen Lebensdauer. Die Disziplin Konfigurationsmanagement bildet den methodischen Ansatz, diese Probleme besser zu beherrschen. Software-Konfigurationsmanagement faßt die Herstellung von Softwaresystemen als eine Abfolge von kontrollierten Änderungen an gesicherten Zwischen- und Endergebnissen auf. Dargestellt werden die Objekte und Funktionen des Software-Konfigurationsmanagements sowie die hierfür in großen Software-Projekten benötigten Methoden, Instanzen und Hilfsmittel.     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and     

Editorial:Brain-machine interface(BMI) and cyborg intelligence

正Brain-machine interfaces(BMIs)aim at building a direct communication pathway between the brain and an external device,and represent an area of research where significant progress has been made during the past decade.Based on BMIs,mind information can be read out by neural signals to control     

Information for Authors

正http://www.zju.edu.cn/jzus http://www.springerlink.com Aim The Journals of Zhejiang University-SCIENCE(A/B/C)are edited by the international board of distinguished Chinese and foreign scientists,and are aimed to present the latest developments and achievements in scientific research in China and overseas to the world’s scientific circles,especially to stimulate     

SCIENCE CHINA Information Sciences SUBJECT INDEX TO VOLUME 57(2014)

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