一种数据网格的元数据分类管理机制研究   总被引：2，自引：0，他引：2  

张建华  关佶红  王雷春  张俊 《武汉大学学报(信息科学版)》2005,30(1):56-60

网格环境下数据发现和访问的关键问题是应用元数据管理和拷贝元数据管理，针对这一问题，提出了一种元数据分类管理机制。该机制定义了数据模型和应用元数据模式，采用了分类存储的方式对不同的元数据进行管理，并设计了一个基于向导的查询智能体。  相似文献   

人工神经网络在天津市区地面沉降预测中的应用   总被引：8，自引：1，他引：8  

李涛  潘云  娄华君  李波  王宏  邹立芝 《地质通报》2005,24(7):677-681

在分析天津市区地面沉降特点的基础上，结合人工神经网络原理，选择1961-1980年的天津市区降水量、地下水开采量、前年沉降量、固结度作为训练样本的输入量，以这20年的地面沉降量作为输出量，用贝叶斯正则化算法训练BP网络，得到沉降的仿真模型。并把1981-1993年的资料用来进行预测检验，结果表明这是一种比较理想的地面沉降预测方法。最后在不同的降水量保证率下，预测了到2010年天津市区地面沉降的情况。  相似文献   

反扩散格式在二维锋生数值模拟中的应用     

Yang Hongwei  Wang Bin  Ji Zhongzhen 《大气科学进展》2002,19(5):863-869

把无粘流中能成功捕捉接触间断的反扩散差分格式，用在了准地转二维理想锋生的数值模拟中，并将计算结果与Cullen的计算结果和半地转方程的精确解进行了比较。用反扩散格式模拟的锋面较Cullen的结果更加显，而且更接近半地转方程的精确解。  相似文献   

吐鲁番山区人工增水作业效果的统计分析     

施文全  王红岩  黄显金  张山青  郑乐娟 《沙漠与绿洲气象(新疆气象)》2002,25(6):28-30

应用统计检验和回归分析两种数理统计方法，对吐鲁番1997－2000年山区人工增水作业效果进行了分析。结果得出作业影响区内作业年份5－6月平均流量显著增加。  相似文献   

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS   总被引：1，自引：0，他引：1       下载免费PDF全文

蒋国荣  张韧  沙文钰  阎俊岳  姚华栋 《Acta Meteorologica Sinica》2002,(1)

In the context of tower measured radiation datasets.following the correction principle meetinga diagnostic equation in data quality control and in terms of a technique for model construction ondata and ANN(artificial neural network)retrieval for BP correction of radiation measurementswith rough errors available,a BP model is presented.Evidence suggests that the developed modelworks well and is superior to a convenient multivariate linear regression model,indicating its wideapplications.  相似文献   

Structural damage detection using the optimal weights of the approximating artificial neural networks     

Shih‐Lin Hung  C. Y. Kao 《地震工程与结构动力学》2002,31(2):217-234

This work presents a novel neural network‐based approach to detect structural damage. The proposed approach comprises two steps. The first step, system identification, involves using neural system identification networks (NSINs) to identify the undamaged and damaged states of a structural system. The partial derivatives of the outputs with respect to the inputs of the NSIN, which identifies the system in a certain undamaged or damaged state, have a negligible variation with different system errors. This loosely defined unique property enables these partial derivatives to quantitatively indicate system damage from the model parameters. The second step, structural damage detection, involves using the neural damage detection network (NDDN) to detect the location and extent of the structural damage. The input to the NDDN is taken as the aforementioned partial derivatives of NSIN, and the output of the NDDN identifies the damage level for each member in the structure. Moreover, SDOF and MDOF examples are presented to demonstrate the feasibility of using the proposed method for damage detection of linear structures. Copyright © 2001 John Wiley & Sons, Ltd.  相似文献   

Modelling evaporation using an artificial neural network algorithm   总被引：1，自引：0，他引：1  

K. P. Sudheer  A. K. Gosain  D. Mohana Rangan  S. M. Saheb 《水文研究》2002,16(16):3189-3202

This paper investigates the prediction of Class A pan evaporation using the artificial neural network (ANN) technique. The ANN back propagation algorithm has been evaluated for its applicability for predicting evaporation from minimum climatic data. Four combinations of input data were considered and the resulting values of evaporation were analysed and compared with those of existing models. The results from this study suggest that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization properties of ANN models unless trained carefully. The study indicated that evaporation values could be reasonably estimated using temperature data only through the ANN technique. This would be of much use in instances where data availability is limited. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

Simulation of artificial random field considering the temporal-spatial variation     

倪永军  朱晞 《地震学报(英文版)》2002,15(4):425-430

Introduction For the seismic design of special structures such as nuclear power station, marine platform, long-span bridge and dam, generally the time-history response analysis of the structure under seismic excitation is imperative, which was coded in most seismic design codes. The earthquake records suitable for the seismic situation and site condition are necessary to be used as the seismic input in the dynamic analysis of structures. As a result of the limited observational condition of st…  相似文献   

Application of genetic BP network to discriminating earthquakes and explosions     

边银菊 《地震学报(英文版)》2002,15(5):540-549

Introduction Artificial Neural Network (ANN) is an important branch of artificial intelligence. It is proposed on the foundation of the study on modern neural science, is a man-made network that can implement some functions based on the mans comprehensive understanding for cerebral neural network (HAN, WANG, 1997). ANN is a mathematical model of simplified human brain neural network and is used to simulate the structures and functions of human brain neural network. ANN is a complex netw…  相似文献   

ARTIFICIAL NEURAL NETWORK CORRECTION OF ROUGH ERRORS OF OBSERVATIONS^*          下载免费PDF全文

JIANG Guorong  ZHANG Ren  SHA Wenyu  YAN Junyue  YAO Huadong 《Acta Meteorologica Sinica》2002,16(1):123-132

In the context of tower measured radiation datasets.following the correction principle meeting a diagnostic equation in data quality control and in terms of a technique for model construction on data and ANN (artificial neural network) retrieval for BP correction of radiation measurements with rough errors available,a BP model is presented.Evidence suggests that the developed model works well and is superior to a convenient multivariate linear regression model,indicating its wide applications.  相似文献