基于MATLAB的主成分RBF神经网络降水预报模型   总被引：13，自引：3，他引：10  

农吉夫  金龙 《热带气象学报》2008,24(6):713-717

以前期500 hPa高度场、海温场为预报因子,采用径向基函数(RBF)神经网络与主成分分析相结合的方法,建立了广西中部5月平均降水预报模型。在5年独立样本的预测检验中,预测的平均相对误差、均方误差及平均绝对误差分别为18.12%、50.52和34.23。对比分析RBF神经网络与BP(Back Propagation)神经网络的预测结果,表明RBF神经网络预测结果更准确、精度更高。  相似文献   

基于贝叶斯网络分类的遥感影像变化检测     

陈雪  马建文  戴芹 《遥感学报》2005,9(6):667-672

遥感成像过程中,地面、大气等诸多要素的不确定性和波段之间的相关性等原因影响了分类精度,导致变化检测的不准确性。为了提高分类精度往往需要引入先验知识。贝叶斯网络是一种新的数据表达和推理模型,对数据没有严格的正态分布前提要求,通过动态地调整先验概率密度,能有效提高分类精度。以北京通州地区1996-05-29和2001-05-19两个时相的陆地卫星Landsat TM遥感影像为例,介绍了基于贝叶斯网络的分类算法,并在此基础上实现了两个时相遥感影像的变化检测。实验结果表明:基于贝叶斯网络分类算法的后分类比较变化检测方法是遥感影像变化检测的一种新的有效方法。  相似文献   

无线传感器网络中基于时钟漂移补偿的时间同步技术   总被引：4，自引：0，他引：4  

刘迪忻 《时间频率学报》2009,32(2):120-128

在分析和总结现有时间同步算法的特点及其存在问题的基础上,提出了一种在无线传感器网络的时间同步协议（TPSN）中同时考虑节点时钟偏移和频率漂移率的改进算法。该算法利用连续两次同步过程中所记录的时间信息来估算节点时钟的偏移和频率漂移率,并进行补偿。同传统算法相比,此算法通讯成本较低、精度较高。最后通过数学分析和仿真的方法对所提出的改进算法进行了验证。  相似文献   

Neural network-based model for assessing failure potential of highway slopes in the Alishan, Taiwan Area: Pre- and post-earthquake investigation     

Hung-Ming Lin  Shun-Kung Chang  Jian-Hong Wu  C. Hsein Juang   《Engineering Geology》2009,104(3-4):280-289

This paper is aimed at creating an empirical model for assessing failure potential of highway slopes, with a special attention to the failure characteristics of the highway slopes in the Alishan, Taiwan area prior to, and post, the 1999 Chi-Chi, Taiwan earthquake. The basis of the study is a large database of 955 slope records from four highways in the Alishan area. Artificial neural network (ANN) is utilized to “learn” from this database. The developed ANN model is then used to study the effect of the Chi-Chi earthquake on the slope failure characteristics in the Alishan area. Significant changes in the degrees of influence of several factors (variables) are found and possible reasons for such changes are discussed. The novelty of this paper lies in the fact that the developed ANN models are used as a tool to investigate the slope failure characteristics before and after the Chi-Chi earthquake.  相似文献   

Prediction of pile settlement using artificial neural networks based on standard penetration test data   总被引：4，自引：0，他引：4  

F. Pooya Nejad  Mark B. Jaksa  M. Kakhi  Bryan A. McCabe 《Computers and Geotechnics》2009,36(7):1125-1133

In recent years artificial neural networks (ANNs) have been applied to many geotechnical engineering problems with some degree of success. With respect to the design of pile foundations, accurate prediction of pile settlement is necessary to ensure appropriate structural and serviceability performance. In this paper, an ANN model is developed for predicting pile settlement based on standard penetration test (SPT) data. Approximately 1000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper compares the predictions obtained by the ANN with those given by a number of traditional methods. It is demonstrated that the ANN model outperforms the traditional methods and provides accurate pile settlement predictions.  相似文献   

Using neural networks for the derivation of Runge-Kutta-Nyström pairs for integration of orbits     

Ch. Tsitouras  I.Th. Famelis 《New Astronomy》2012,17(4):469-473

In this paper we present Runge-Kutta-Nyström (RKN) pairs of orders 4(3) and 6(4). We choose a test orbit from the Kepler problem to integrate for a specific tolerance. Then we train the free parameters of the above RKN4(3) and RKN6(4) families to perform optimally. For that we form a neural network approach and minimize its objective function using a differential evolution optimization technique. Finally we observe that the produced pairs outperform standard pairs from the literature for Pleiades orbits and Kepler problem over a wide range of eccentricities and tolerances.  相似文献   

Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations     

R. Qahwaji  T. Colak  M. Al-Omari  S. Ipson 《Solar physics》2008,248(2):471-483

Machine-learning algorithms are applied to explore the relation between significant flares and their associated CMEs. The NGDC flares catalogue and the SOHO/LASCO CME catalogue are processed to associate X and M-class flares with CMEs based on timing information. Automated systems are created to process and associate years of flare and CME data, which are later arranged in numerical-training vectors and fed to machine-learning algorithms to extract the embedded knowledge and provide learning rules that can be used for the automated prediction of CMEs. Properties representing the intensity, flare duration, and duration of decline and duration of growth are extracted from all the associated (A) and not-associated (NA) flares and converted to a numerical format that is suitable for machine-learning use. The machine-learning algorithms Cascade Correlation Neural Networks (CCNN) and Support Vector Machines (SVM) are used and compared in our work. The machine-learning systems predict, from the input of a flare’s properties, if the flare is likely to initiate a CME. Intensive experiments using Jack-knife techniques are carried out and the relationships between flare properties and CMEs are investigated using the results. The predictive performance of SVM and CCNN is analysed and recommendations for enhancing the performance are provided.  相似文献   

Configurations of community in flood risk management     

Aleksi Räsänen  Vera Kauppinen  Sirkku Juhola  Gunhild Setten  Haakon Lein 《Norsk geografisk tidsskrift. Norwegian journal of geography》2020,74(3):165-180

ABSTRACT

Despite a notable increase in the literature on community resilience, the notion of ‘community’ remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.  相似文献   

全球生产网络与本土创新网络的战略耦合动态——以中国生物医药产业为例   总被引：1，自引：0，他引：1  

王璐玮  汪涛  张晗 《地理研究》2021,40(12):3314-3332

构建显示性比较优势指数,分析“走出去”全球化下中国医药产业的国际竞争力;识别企业战略连接类型,分析“引进来”全球化下生物医药全球生产网络（GPN）与本土创新网络（IIN）间的战略耦合过程;运用超效率SBM模型,测度GPN-IIN战略耦合目标实现的达成效率,并从静、动态视角探究其时空变化的原因。发现：① 中国医药产业通过转口、代加工进入北美市场的难度加大,东盟、金砖国家成为其重要的出口对象,与欧盟加强贸易有助于其形成高标准的自由贸易协定网络。② 2000—2006年,多数外商直接投资（FDI）企业形成“两头在外”的产业布局,在GPN中处于被俘虏地位,大多内资企业学习能力不足,处于IIN边缘。2007—2013年,部分FDI企业由GPN价值链末端向高端转移,部分内资企业由IIN边缘向核心转移,但转移过程中具有强松脚性。2014—2019年,GPN-IIN战略耦合克服上一阶段嵌入不足、松脚性等问题,两者间的关系通道日益增多和稳固。③ GPN-IIN耦合绩效经历了“大分散、小集聚→简单结构孕育→局部结构构建→级联秩序分异”的演变,在此过程中技术进步对其促进效果最强,规模效率次之,纯技术效率最弱。而限制其提升的主要原因从价值链低端锁定、企业类型晋升不足向企业类型退化冗余、社会关系松弛,再向境外业务拓展乏力、资本-技术脱钩过渡。  相似文献   

多尺度视角下中国新能源汽车产业创新空间格局及网络特征   总被引：1，自引：1，他引：0  

张凯煌  千庆兰  陈清怡 《地理科学进展》2021,40(11):1824-1838

创新活动与创新网络的多尺度空间模式是创新经济地理学关注的焦点。论文选择在知识分类上兼具隐性和显性知识特征的新能源汽车产业作为研究对象,利用来自国家知识产权局的相关专利数据,分析了2001—2015年中国新能源汽车产业独立创新活动和合作创新网络在“区域—省域—市域”的多尺度空间特征,并对其进行空间分类。结果显示：① 创新活动在城市层面最具解释力度,中国新能源汽车产业技术创新活动主要集中在东部和北部沿海地区,市域尺度上的创新活动存在较大差异;② 内部创新网络和外部创新网络在区域层面最具解释力度,创新网络集中在东部和北部沿海地区,市域尺度上的创新网络变异明显;③ 在不同尺度上,创新网络与创新活动之间存在不同程度的差异。地方创新环境与创新主体性质是造成上述格局差异的重要原因。仅停留在空间上的分析很难深入解释创新网络格局,应从更微观的创新主体层面探究创新结网的过程和机理。此外,创新活动和创新网络分别具有“多尺度结构性嵌套”和“多尺度能动性交换”特征,未来研究应对两者之间的差别给予更多关注。  相似文献