基于可分解马尔科夫网的极端椒盐噪声图像滤波   总被引：4，自引：0，他引：4  

曹建农  李德仁  关泽群 《武汉大学学报(信息科学版)》2005,30(7):583-587

提出了一种基于可分解马尔科夫网(decomposableMarkovnetworks,DMN)的极端椒盐噪声的均值滤波方法,指出其网络节点的阈值衰减特性和网络节点的连接特性具有很好的对椒盐噪声污染图像的噪声定位的作用,并提出一种在该网络控制下,只对与噪声相关的像素进行均值计算以替代噪声像素的亚均值滤波算法,实现了图像的较强自适应滤波。实验表明,本文方法具有良好的滤波性能。  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   

基于气象因子的华中电网负荷预测方法研究   总被引：7，自引：0，他引：7       下载免费PDF全文

胡江林  洪斌等 《应用气象学报》2002,13(5):600-608

在分析各种节假日负荷变化规律的基础上, 利用气象因子作预报变量, 使用动态的综合线性回归和自回归相结合的混合线性回归方法及非线性的人工神经网络方法来进行华中电网日负荷和日最大负荷及日最小负荷的预测。对12个月共365天的独立样本试预报表明, 该客观方案对华中电网负荷的预测精度可满足业务调度的需要。  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   

利用ERS-2 SAR图像纹理分析方法揭示长白山天池火山近代喷发物空间分布特征   总被引：4，自引：0，他引：4       下载免费PDF全文

单新建  叶洪  陈国光 《第四纪研究》2002,22(2):123-130

简单介绍了SAR图像的纹理特征以及正交小波变换纹理提取方法。论述了SAR图像的纹理特征参与分类的重要性。以长白山天池火山为例,通过对ERS2SAR图像进行纹理分析,提取了SAR图像两个层次的尺度变化、时频局部化和方向性纹理特征。并将SAR纹理特征与TM图像及DEM进行复合,利用多源信息各自的优势,进行了BP神经元网络分类,从较大范围对长白山天池火山735±15aB.P.大喷发的喷发物空间分布进行评价。获取了长白山天池火山近代喷发物的空间分布及规模。这对长白山天池火山未来喷发危险性初步评价、火山地质制图及火山灾害预测有重要意义。  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   

Fuzzy neural network models for liquefaction prediction   总被引：1，自引：0，他引：1  

M. S. Rahman  Jun Wang 《Soil Dynamics and Earthquake Engineering》2002,22(8)

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage, the training case histories are used to determine initial network parameters. In the final training stage, the training case histories are processed one by one to develop membership functions for the network parameters. During the testing phase, input variables are described in linguistic terms such as ‘high’ and ‘low’. The prediction is made in terms of a liquefaction index representing the degree of liquefaction described in fuzzy terms such as ‘highly likely’, ‘likely’, or ‘unlikely’. The results from the model are compared with actual field observations and misclassified cases are identified. The models are found to have good predictive ability and are expected to be very useful for a preliminary evaluation of liquefaction potential of a site for which the input parameters are not well defined.  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