Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation

This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft computing techniques and global navigation satellite system/leveling measurements. Artificial neural networks, fuzzy logic, and least square support vector machine models are developed and used to fit the global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is designed and evaluated based on the latest global geoid models. The results of the three estimation models show that they can be used to correct the shoreline geoid model, in terms of root mean square error that ranges from 1.7 to 8.5?cm. Moreover, it is found that the least square vector machine model is a competitive approach with certain advantage in solving complex problems represented by missing data.     

用遗传算法对自动控制系统数学模型寻优

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。     

优化发电能源结构的初探

从我国电力工业发展的角度，浅析了国内外发电能源结构及其变化趋势，作者认为优化我国发电能源结构、采取必要的技术措施，是实现我国电力 工业可持续发的重要途径。     

Prediction of Stellar Atmospheric Parameters using Instance-Based Machine Learning and Genetic Algorithms

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.     

基于可靠度理论的黄土高边坡优化设计

对黄土高边坡进行可靠性分析，应用数学原理和优化原理，建立了黄土高边坡的优化模型，对铜黄一级公路黄土高边坡进行分析验证。结果表明，该方法计算的结果与实际较接近．应用中易操作。     

LAMOST总控系统程序设计

国家重大科学工程项目LAMOST总控系统的程序设计是一个庞大的软件工程．其总控系统下共包括8个既相互独立又互有关联的子系统．如何通过良好的软件设计方法来处理好这8个子系统之间的关系使其协调运转，以及如何确保软件开发的可维护性、可持续性是研究重点．     

形状误差圆柱度的优化计算

建立了简明的圆柱度数学模型，并采用优化方法进行求解，为圆柱度误差值的评定提供了较理想的计算方法。     

黄土斜坡稳定性的模糊信息分析法

本文基于黄土斜坡稳定性的实际资料,利用信息分配、模糊一二级近似推论及信息集中等原因建立了一套黄土斜坡稳定性的评价方法。用这套方法对一些黄土斜坡(非母体)稳定性进行了验证,其结果与实际情况一致。本方法已应用于兰州市滑坡防灾规划和皋兰山三台阁北侧几个滑坡及斜坡稳定性的研究中,效果良好。     

FUZZY COMPREHENSIVE EVALUATION OF CONTAINER TRANSPORTATION MODES ALONG THE CHANGJIANG RIVER MAIN LINE AND ITS DELTA AREA

The Changjiang River (Yangtze) is one of the fastest growth areas of container transportation in Chi-na. Rail, road and water transportation have competed against each other for container transportation in the Chang-jiang River main line and its delta area. It is of significance to assess these different transportation modes scientifi-cally in order to organize container transportation efficiently in this area and make decision for integral plan and construction of transportation system in this area. This paper outlines application of fuzzy comprehensive evaluation to appraise different modes of typical direction of containers. Twelve assessment indexes were decided. Membership functions were formulated. Evaluation results indicated that road transportation was optimal mode in the Changjiang River delta area, however water transportation was the primary way in the Changjiang River main line.     

模糊PID控制器在水压机控制系统中的应用

介绍了模糊控制中的设计思想及方法，并采用模糊控制的方法来改进实际生产中PID控制的一些控制难点。将改进方法应用在一大型水压机控制系统中并在实际生产中取得了理想效果。