基于粒子群算法优化BP神经网络的地铁暗挖段地表变形预测

在使用传统BP神经网络算法建模进行预测过程中,由于初始权值和阈值是随机给定的,易使网络陷入局部最优,从而导致预测精度较低。利用具有较强优化能力的粒子群算法（ particle swarm optimization ,PSO）优化BP神经网络在训练过程中的初始权值和阈值,建立新的预测模型,以青岛地铁3号线保河区间隧道监测数据为例进行验证分析,研究结果表明,与传统BP神经网络预测算法相比,使用PSO算法优化的BP神经网络预测算法可以得到更优的预测结果。     

关于山区道路工程控制网布设方案研究

对于贵州山区而言,多数道路工程测区中通常是山峦起伏、荆棘丛生,致使通视、通行条件差,因此,建立满足工程施工需要和符合控制精度要求并能尽量减少工作量的测量控制网是很有研究价值的。从通视性分析和网形优化的角度提出控制网的布设方案,希望对今后的此类项目实施起到一定的借鉴作用。     

基于网络Voronoi图启发式和群智能的最大覆盖空间优化

提出一种基于网络Voronoi面域图的最大覆盖选址模型及相应的粒子群优化方法,并应用于城市响应时间敏感型公共服务设施的空间优化。本文考虑设施功能沿交通网络辐射以及需求非均匀分布情形,对设施在网络连续空间上进行布局优化,选址模型采用网络Voronoi面域图划分布局设施的功能辐射域,以启发空间优化最小化重叠覆盖。模型同时顾及了设施利用率的最大化,规定设施对给定距离以内的需求实行的完全服务覆盖和对给定距离以外的需求实行随距离衰减的部分服务覆盖。本研究提出基于遗传机制和广义Voronoi图改进的粒子群算法以提高其空间优化性能,通过对南京市消防站最大覆盖空间优化实验表明,该研究取得了较为理想的结果,可应用于城市化区域应急设施最大覆盖空间优化。     

基于Creator、3DSMax和ArcEngine的三维城市建模方法

通过Creator和3DSMax联合使用构建三维建筑模型,在GIS中对原始影像进行了纠正,并生成了Ter-rain地形文件,应用ArcEngine和VS 2008（C#）进行二次开发,实现了银川市三维城市浏览与管理。     

第二次全国土地调查成果的多比例尺缩编方法研究

第二次全国土地调查于2009年底基本完成,本文针对其成果比例尺单一、不能满足各类不同使用目的的用图要求的问题,提出二调成果的多比例尺缩编方法,并采用语义融合、多义线一体化综合、改进的2维Douglas-Peucker算法等相结合策略,高效准确地完成了缩编任务,对全国其他地方刚刚开展的同类型工作提供了一种较好的思路和借鉴。     

多目标空间决策中交互式辅助决策支持模型研究

现实世界中普遍存在着多目标空间优化问题.本文主要对Pareto解集的分析和评价进行研究,提出了一种交互式辅助决策支持模型,它主要包括3个核心部分,即地理刷新机制、相似性查询算子以及交互式搜索方法.然后,本文基于此思想设计了原型系统并在实践中得到了较成功的检验.     

CINRAD/CC雷达速调管运行状态控制

速调管性能直接影响到雷达探测的回波质量、运行成本和使用时间,目前使用和维护好速调管是雷达站提高业务质量和降低成本的主要环节之一.从云南6部CINRAD/CC雷达近年来的运行情况看,使用和维护好速调管是十分重要和必要的.以德宏、普洱的速调管使用维护为个例,在速调管工作原理的基础上,结合前期实际工作中经验,以收集极波形、发射功率和地物回波变化三项指标来判断运行性能状况.通过多年分析总结,电子枪通常是速调管的易损和易老化部件,对此主要提出速调管使用初期宜适当调高电子枪灯丝电流以使灯丝充分激活和保证新管具有较好的真空度;中期宜灯丝电流略低运行;晚期速调管的阴极发射能力可能会有所下降,在此时可又适当调高灯丝电流,以保证雷达探测的回波质量.通过对速调管工作阶段提出分段运行的新措施和建议,从实际效果看,在不影响探测质量的前提下应能提高速调管的使用时间,供同行探讨.对其他型号CINRAD雷达也应有较好的借鉴.     

ABP法在高密度电阻率法反演中的应用

非线性反演方法作为地球物理反演的一个重要分支,在地球物理反演中发挥着特有的作用.近年来学者对非线性联合反演研究较多,但目前仍未有实质性的研究进展;本文尝试利用BP(Back Propagation)神经网络优化方法与蚁群算法联合演算,实现高密度电阻率法的电阻率二维非线性反演.通过两组模型的结果比较,BP与ABP 法的反...     

Solutions of the inherent problem of the equivalence in direct current resistivity and electromagnetic methods through global optimization and joint inversion by successive refinement of model space

The problem of equivalence in direct current (DC) resistivity and electromagnetic methods for a thin resistive and conducting layer is well‐known. Attempts have been made in the past to resolve this problem through joint inversion. However, equivalence still remains an unresolved problem. In the present study, an effort is made to reduce non‐uniqueness due to equivalence using global optimization and joint inversion by successive refinement of the model space. A number of solutions derived for DC resistivity data using very fast simulated annealing global inversion that fits the observations equally well, follow the equivalence principle and show a definite trend. For a thin conductive layer, the quotient between resistivity and thickness is constant, while for a resistive one, the product between these magnitudes is constant. Three approaches to obtain very fast simulated annealing solutions are tested. In the first one, layer resistivities and thicknesses are optimized in a linear domain. In the second, layer resistivities are optimized in the logarithmic domain and thicknesses in the linear domain. Lastly, both layer resistivities and thicknesses are optimized in the logarithmic domain. Only model data from the mean models, corresponding to very fast simulated annealing solutions obtained for approach three, always fit the observations. The mean model defined by multiple very fast simulated annealing solutions shows extremely large uncertainty (almost 100%) in the final solution after inversion of individual DC resistivity or electromagnetic (EM) data sets. Uncertainty associated with the intermediate resistive and conducting layers after global optimization and joint inversion is still large. In order to reduce the large uncertainty associated with the intermediate layer, global optimization is performed over several iterations by reducing and redefining the search limits of model parameters according to the uncertainty in the solution. The new minimum and maximum limits are obtained from the uncertainty in the previous iteration. Though the misfit error reduces in the solution after successive refinement of the model space in individual inversion, it is observed that the mean model drifts away from the actual model. However, successive refinement of the model space using global optimization and joint inversion reduces uncertainty to a very low level in 4–5 iterations. This approach works very well in resolving the problem of equivalence for resistive as well as for conducting layers. The efficacy of the approach has been demonstrated using DC resistivity and EM data, however, it can be applied to any geophysical data to solve the inherent ambiguities in the interpretations.     

基于能量的高层建筑分段隔震体系参数优化

针对带转换层复杂高层建筑抗震性能较差的缺点,提出新型的分段隔震体系.本文从随机振动理论和能量平衡原理出发,建立了基于能量的分段隔震结构被动控制参数优化方法.通过MATLAB编程对算例进行多工况参数分析,获得了控制参数对隔震层与地震总输人能量的比值的影响规律:中间隔震层位于结构中部附近时,存在最优频率比和阻尼比使得隔震层...