2-D SHAPE BLENDING OF NURBS CURVE SHAPES

２－ＤＳＨＡＰＥＢＬＥＮＤＩＮＧＯＦＮＵＲＢＳＣＵＲＶＥＳＨＡＰＥＳＪｉｎＸｉａｏｇａｎｇ；ＢａｏＨｕｊｕｎ；ＰｅｎｇＱｕｎｓｈｅｎｇ２－ＤＳＨＡＰＥＢＬＥＮＤＩＮＧＯＦＮＵＲＢＳＣＵＲＶＥＳＨＡＰＥＳ￥ＪｉｎＸｉａｏｇａｎｇ；ＢａｏＨｕｊｕｎ；Ｐｅ...     

一个基于实例推理的专家系统

该文研究了基于实例推理（Ｃａｓｅ－ＢａｓｅｄＲｅａｓｏｎｉｎｇ,简称ＣＢＲ）的机制,重点讨论了其在商业ＭＩＳ框架生成专家系统ＦＧＳＭ中的应用,结合ＦＧＳＭ系统的研制,给出了ＣＢＲ的一般实现过程。     

抛物型方程有限差分并行解法

抛物型方程有限差分并行解法张宝琳，陈劲（北京应用物理与计算数学所）ＰＡＲＡＬＬＥＬＳＯＬＵＴＩＯＮＯＦＰＡＲＡＢＯＬＩＣＥＱＵＡＴＩＯＮＳＢＹＦＩＮＩＴＥＤＩＦＦＥＲＥＮＣＥＭＥＴＨＯＤＳ￥ＺｈａｎｇＢａｌｉｎ；ＣｈｅｎＪｉｎ（Ｉｎｓｔｉｔｕｔｅｏｆ...     

BOIF-CIMS实施中的关键技术及解决

该文介绍了ＢＯＩＦ－ＣＩＭＳ工程开展的背景和情况,给出了基于ＰＤＭ构架的ＣＩＭＳ集成体系结构。对ＢＯＩＦ－ＣＩＭＳ开发和实施中的一些关键技术及解决办法进行了讨论,并着重对ＢＯＩＦ－ＣＩＭＳ系统中多应用系统情况下的信息集成技术及数据流进行了阐述。     

一种基于对象的分布式系统描述求精方法

一种基于对象的分布式系统描述求精方法杜兴，谢立，孙钟秀（南京大学计算机科学与技术系南京２１０００８）ＡＮＯＢＪＥＣＴ－ＢＡＳＥＤＡＰＰＲＯＡＣＨＴＯＳＰＥＣＩＦＹＡＮＤＲＥＦＩＮＥＤＩＳＴＲＩＢＵＴＥＤＳＹＳＴＥＭＳ￥ＤｕＸｉｎｇ；ＸｉｅＬｉａｎｄＳ...     

COMPUTER GRAPHIC ASSEMBLY TECHNOLOGY FOR COMPOUND-BODY

ＣＯＭＰＵＴＥＲＧＲＡＰＨＩＣＡＳＳＥＭＢＬＹＴＥＣＨＮＯＬＯＧＹＦＯＲＣＯＭＰＯＵＮＤ－ＢＯＤＹ￥ＳｈａｎｇＪｉａｎｚｈｏｎｇ；ＰａｎＣｕｎｙｕｎ（ＤｅｐａｒｔｍｅｎｔｏｆＰｒｅｃｉｓｉｏｎＭａｃｈｉｎａｒｙａｎｄｉｎｓｔｒｕｍｅｎｔＮａｔｉｏｎａ...     

CYB—YB—F（B,D）型应变传感器的力学分析

本文利用ＳＵＰＥＲＳＡＰ程序对ＣＹＢ－ＹＢ－Ｆ（Ｂ．Ｄ）型应变传感器进行有限元计算，给出该醋 的变形规律，指出应变片粘贴的最佳位置。以及该传感器的自振频率，并和实验结果进行比较。     

SHAPE PRESERVING INTERPOLATION USING PIECEWISE CUBIC

ＳＨＡＰＥＰＲＥＳＥＲＶＩＮＧＩＮＴＥＲＰＯＬＡＴＩＯＮＵＳＩＮＧＰＩＥＣＥＷＩＳＥＣＵＢＩＣＦａｎｇＫｕｉ；ＴａｎＪｉａｎｒｏｎｇＳＭＯＯＴＨＳＵＲＦＡＣＥＩＮＴＥＲＰＯＬＡＴＩＯＮＯＶＥＲＡＲＢＩＴＲＡＲＹＴＲＩＡＮＧＵＬＡＴＩＯＮＳＢＹＳＵＢＤ...     

基于方块脉冲函数逼近的线性连续回归模型的参数估计及其应用

基于方块脉冲函数逼近的线性连续回归模型的参数估计及其应用赵明旺（武汉钢铁学院）ＰＡＲＡＭＥＴＥＲＥＳＴＩＭＡＴＩＯＮＦＯＲＬＩＮＥＡＲＣＯＮＴＩＮＵＯＵＳＲＥＧＲＥＳＳＩＶＥＳＹＳＴＥＭＳＶＩＡＢＬＯＣＫＰＵＬＳＥＦＵＮＣＴＩＯＮＳＡＮＤＩＴＳＡＰＰ...     

FKCN优化的RBF神经网络

ＦＫＣＮ（Ｆｕｚｚｙ Ｋｏｈｏｎｅｎ ｃｌｕｓｔｅｒ ｎｅｔｗ ｏｒｋ）将模糊隶属度的概念用于Ｋｏｈｏｎｅｎ 神经网络的学习和更新策略中,改善了Ｋｏｈｏｎｅｎ 网络的性能,是一种更为快速有效的聚类网络。作者将ＦＫＣＮ用于优化ＲＢＦ（Ｒａｄｉａｌｂａｓｉｃ ｆｕｎｃｔｉｏｎ）神经网络基函数的中心,并将优化后的ＲＢＦ网络用于曲线拟合和非线性时间序列预测,同时与基于Ｃ－ＭＥＡＮＳ的ＲＢＦ网络进行比较。实验结果表明：采用ＦＫＣＮ优化的ＲＢＦ网络具有更好的拟合和预测能力,尤其在曲线拟合实验中,ＦＫＣＮ优化的ＲＢＦ网络可以达到最小学习误差,比Ｃ－ＭＥＡＮＳ的网络小一个数量级,可见用ＦＫＣＮ优化ＲＢＦ神经网络可以较好地提高ＲＢＦ神经网络的性能。     

基于模糊聚类和直觉模糊推理的合成旅油料需求预测

需求预测是合成旅组织油料保障的基础环节,对合成旅成功遂行军事行动有着比较重要的影响.由于合成旅组成结构的特殊性,传统预测方法存在较大弊端,因此,提出了基于模糊聚类和直觉模糊推理的合成旅油料需求预测方法.首先,通过模糊C均值聚类算法实现对历史案例的初步筛选,以提高案例检索速度.然后,构建了案例特征属性的主客观综合权重模型和基于直觉模糊集的案例检索模型,保证了案例检索的准确度.最后,构建了基于整体数据特征的合成旅油料需求预测模型.通过算例分析验证上述预测方法的可行性和实用性,证明了该方法有助于提高检索速度和预测准确度.     

图书文献经费的支持向量机组合预测

对灰色、神经网络和SVM（支持向量机）的3个预测模型进行了研究,以某图书馆1996年～2003年图书文献总经费为例,对图书文献总经费进行了预测,经过比较,SVM的预测方法精度较高。在分析组合预测特性的基础上,提出了对灰色系统、神经网络和SVM三种预测方法结果进行了线性组合预测方法和SVM的组合预测方法。与单一预测方法结果和线性组合预测进行对比,SVM组合预测方法比较精确。     

基于支持向量机的科技图书市场预测

对灰色、神经网络和支持向量机的三个预测模型进行研究,以某某类科技图书1993-2000年的年发行量为例,对科技图书市场进行预测,经过比较,支持向量机的预测方法精度较高。本方法可推广应用于其他类图书市场的预测。     

Forecasting and Prediction Applications in the Field of Power Engineering

Within the field of power engineering, forecasting and prediction techniques underpin a number of applications such as fault diagnosis, condition monitoring and planning. These applications can now be enhanced due to the improved forecasting and prediction capabilities offered through the use of artificial neural networks. This paper demonstrates the maturity of neural network based forecasting and prediction through four diverse case studies. In each case study the authors have developed diagnostic, monitoring or planning applications (within the power engineering field) using neural networks and industrial data. The engineering applications discussed in the paper are: condition monitoring and fault diagnosis applied to a power transformer; condition monitoring and fault diagnosis applied to an industrial gas turbine; electrical load forecasting; monitoring of the refuelling process within a nuclear power station. For each case study the data sources, data preparation, neural network methods and implementation of the resulting application is discussed. The paper will show that the forecasting and prediction techniques discussed offer significant engineering benefits in terms of enhanced decision support capabilities.     

ARMA序列的版参数预报*

在关于ARMA序列新息定理的基础上,本文给出了ARMA序列的一种半参数预报方法,该方法避开对ARMA模型中MA参数作估计这一计算复杂的工作,在只须知道AR参数的情况下直接得到序列的预报值,从而使计算量大为简化同时又保证有一定的精度,本文还介绍了半参数预报方法在城市自来水负荷预报中的成功应用。     

一种光伏电池组件的温度预测方法

组件温度是影响太阳能电池组件转换效率的一个重要因素,对组件温度的准确预测,有助于提高光伏发电功率预测的精度。文中提出了一种光伏电池组件温度预测方法。该方法考虑了影响光伏电池组件温度的主要因素,通过选择适当的样本建立统计模型。算例结果表明,该预测模型能够较好地对光伏电池组件温度进行预测,具有潜在的应用价值。     

A secondary decomposition based hybrid structure with meteorological analysis for deterministic and probabilistic wind speed forecasting

Accurate wind speed forecasting could ensure the reliability and controllability for the wind power system. In this paper, a new hybrid structure based on meteorological analysis is proposed for the wind speed vector (wind speed and direction) deterministic and probabilistic forecasting. Twelve kinds of secondary decomposition methods are employed to decrease the interference existing in the data. To improve the training efficiency and accelerate the sample selection process, active learning is employed. Four different wind speed datasets collected from Ontario Province, Canada, are utilized as case studies to evaluate the forecasting performance of the proposed structure. Experimental results show that the proposed structure based on meteorological analysis is suitable for wind speed vector forecasting and could obtain better forecasting performance. Furthermore, except accurate deterministic forecasts, the proposed structure also provides more probabilistic forecasting information.     

A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

The internal structure of a complex system can manifest itself with correlations among its components. In global business, the interactions between different markets cause collective lead–lag behavior having special statistical properties which reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic algorithm (GA) with neural network (NN) is proposed to take advantage of the lead–lag dynamics, to make the NN forecasting process more transparent and to improve the NN’s prediction capability. Two business case studies are carried out to demonstrate the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting. Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.