基于PSR-WSVM模型的边坡位移预测     

李建新  刘小生  肖钢  周文  刘仁志 《大地测量与地球动力学》2020,40(6):577-580

为建立高精度的边坡位移预测模型，采用相空间重构（PSR）将边坡位移时间序列数据转换为多维数据，同时构造小波核函数改进的支持向量机模型，建立PSR-WSVM模型并应用于边坡位移预测。将PSR-WSVM模型预测结果与传统支持向量机（SVM）模型、小波支持向量机（WSVM）模型和基于相空间重构的支持向量机（PSR-SVM）模型预测结果进行对比，通过平均绝对误差（MAE）、平均绝对误差百分比（MAPE）和均方根误差（RMSE）3个精度评价指标验证PSR-WSVM模型的可行性。工程实例结果表明，PSR-WSVM模型预测结果的3个精度评价指标都优于另外3种模型，边坡位移预测的精度明显提升。  相似文献   

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing     

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.  相似文献   

基于蚁群算法的城市可持续发展综合评价   总被引：1，自引：0，他引：1  

徐婷婷  于扬  彭娇婷 《成都信息工程学院学报》2006,21(5):694-697

在可持续发展体系分类的基础上，应用蚁群算法对可持续发展各分类指数公式和综合指数公式进行优化，建立可持续发展评价模型。将该模型应用于乌鲁木齐市可持续发展水平评价，评价结果与现状分析一敛。结果表明该模型简单适用、有良好的通用性。  相似文献   

分组密码的安全性与强化设计     

刘峰 《成都信息工程学院学报》2003,18(4):376-380

分组密码是数据通讯中最常用的数据加密方式，以DES为例分析现有分组加密算法的安全隐患，并提出了可变密钥加密和变长密文输出两个新思路，可应用于所有现有分组加密算法以提高安全性，并就该方法的安全性、效率、具体应用做出了分析。  相似文献   

Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning     

Olac Fuentes 《Experimental Astronomy》2001,12(1):21-31

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.  相似文献   

用遗传算法对自动控制系统数学模型寻优     

万月珍  廖成旺 《大地测量与地球动力学》2001,21(2):87-93

探讨用遗传算法对数学模型进行优化。考虑到控制系统稳健性的要求 ,用遗传算法寻找出控制系统最佳稳定域 ,实现控制系统数学模型的寻优。阐述了用遗传算法求解问题的步骤和参数的取值 ,并用仿真实验对优化结果进行了检验。结果表明 :在控制系统数学模型的优化中 ,遗传算法具有其他算法无可比拟的优越性。  相似文献   

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms   总被引：1，自引：0，他引：1  

J. Federico Ramírez  Olac Fuentes  Ravi K. Gulati 《Experimental Astronomy》2001,12(3):163-178

In this article we present a method for the automated prediction of stellar atmospheric parameters from spectral indices. This method uses a genetic algorithm (GA) for the selection of relevant spectral indices and prototypical stars and predicts their properties, using the k-nearest neighbors method (KNN). We have applied the method to predict the effective temperature, surface gravity, metallicity, luminosity class and spectral class of stars from spectral indices. Our experimental results show that the feature selection performed by the genetic algorithm reduces the running time of KNN up to 92%, and the predictive accuracy error up to 35%. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

Three-dimensional discontinuous deformation analysis (3-D DDA) using a new contact resolution algorithm     

S. Amir Reza Beyabanaki  Roozbeh Geraili Mikola  Kianoosh Hatami   《Computers and Geotechnics》2008,35(3):346-356

This paper presents a new contact calculating algorithm for contacts between two polyhedra with planar boundaries in the three-dimensional discontinuous deformation analysis (3-D DDA). In this algorithm, all six type contacts in 3-D (vertex-to-face, vertex-to-edge, vertex-to-vertex, face-to-face, edge-to-edge, and edge-to-face) are simply transformed into the form of point-to-face contacts. The presented algorithm is a simple and efficient method and it can be easily coded into a computer program. In this paper, formulations of normal contact, shear contact and frictional force submatrices based on the new method are derived and the algorithm has been programmed in VC⁺⁺. Examples are provided to demonstrate the new contact rule between two blocks.  相似文献   

流域水文模型参数识别的现代优化方法研究进展   总被引：1，自引：1，他引：0  

武震  张世强  张小文 《冰川冻土》2008,30(1):64-71

水文参数优化是水文模型发展中的一个重要部分,针对水循环过程模拟中的流域水文模型参数识别的复杂优化问题,回顾了水文模型优化算法的发展历程,对国内外水文模型参数识别算法的最新进展进行了阐述.介绍了模拟退火算法、遗传算法、SCE-UA、粒子群算法等常用算法的参数设定及一般流程,并对编码遗传算法、单纯形混合加速遗传算法和运用融合技术的几种遗传算法以及其它几种常用算法在新安江模型中的优化结果进行了比较,认为混合加速遗传算法是一种较好的方法.在系统研究现代优化算法与传统优化算法的基础上,建立各种优化算法的融合技术和法则可能是进一步提高参数优化算法性能的方向.  相似文献   

基于AGA的SVM需水预测模型研究   总被引：1，自引：0，他引：1  

张灵  陈晓宏  刘丙军  王兆礼 《水文》2008,28(1):38-42,46

需水预测是一个由城市人口、工业水平、社会经济水平共同作用的多因素、多层次的复杂非线性系统.其结果将直接影响受区域水资源承载力约束的产业结构、布局形态等决策.作为一种集中参数预报方法,支持向量机方法具有对未来样本的较好的泛化性能,对于这类资料缺乏、系统结构尚欠清晰的问题可以取得较好的模拟和预测结果.基于此,本文将支持向量机方法引入需水预测领域,建立了需水预测支持向量机模型.同时,本文将加速遗传算法和支持向量机方法耦合起来,构造了支持向量机模型参数的自适应优化算法.模型在珠海市的应用实例表明:与简单遗传算法比较,AGA的模型参数寻优效率更高;与BP神经网络模型相比,SVM模型较好地解决了小样本、经验性等问题,并取得了较高的预测精度.  相似文献