共查询到16条相似文献，搜索用时 265 毫秒
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瞬变电磁圆锥型场源装置有效的减小了线圈间的电感,提高了小装置探测地下浅层的分辨率,但常规反演方法需给定初始模型且反演精度不高.针对瞬变电磁法反演计算问题,通过对粒子群优化算法(PSO)和神经网络算法(BP)分析研究,改进了一种基于神经网络算法Sigmoid函数的自适应加权粒子群优化(AWPSO)算法.采用标准测试函数对算法进行试算,建立多个理论层状地质模型对该算法进行理论验证,最后在地质资料已知地区开展现场实验.计算结果表明,新提出的算法具有更高的全局搜索寻优能力和收敛速度快、计算精度高,且不需要给定初始模型;实验结果显示实测数据反演结果与高密度电法探测结果吻合,证明该算法能够对瞬变电磁探测数据进行反演计算且精度较高,可以在同类型的浅层探测任务中提供参考. 相似文献
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变异函数是地统计学中区域化变量空间结构分析和空间局部插值的主要分析工具.理论变异函数模型的获取是地质统计学中的基础性工作,它是了解区域化变量的变异特征、进一步对地质统计学计算的必要环节.针对现有的理论变异函数的拟合方法,如人工拟合法、线性规划拟合法、加权多项式拟合法、目标规划拟合法等的不足之处,充分利用粒子群优化算法在求解非线性优化问题时具有的全局寻优的特点,提出基于粒子群优化的理论变异函数拟合方法.在实例应用中,分别利用粒子群优化算法和加权多项式拟合方法进行理论变异函数拟合,交叉验证结果表明粒子群优化算法预测精度较高,具有较强的稳健性. 相似文献
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磁法反演属于非线性最优化问题,具有多变量、目标函数多极值、反演多解性等特点,因此,需要稳定的和高效的优化反演算法.粒子群优化已开始被用于地球物理反演计算,但是对于高维数、多峰值函数,粒子群的收敛精度不高,容易陷入局部极值.如果将混沌局部搜索和粒子群优化的优势相结合,通过将种群搜索过程对应为混沌轨道的遍历过程,可使标准粒子群优化的搜索过程具有避免陷入局部极小的能力.本文利用混沌-粒子群优化用于磁法反演计算.数据试验结果表明,该方法可以用于磁法数据的地球物理非线性反演,并且在一定程度上优于标准粒子群优化方法. 相似文献
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基于粒子群算法,以钢管混凝土框架的层间位移角及构件内力为约束条件、防屈曲支撑核心单元横截面积总和为目标函数,采用Matlab编写了适用于钢管混凝土减震框架的优化算法,并对钢管混凝土减震框架进行了优化分析。结果表明:采用粒子群算法对钢管混凝土减震框架进行参数优化是可行的。粒子群算法参数少、收敛速度快,是一种适用于钢管混凝土减震框架的新型优化方法。 相似文献
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《地震工程与工程振动》2021,41(2)
粒子群优化算法是模拟群体智能所建立起来的一种全局优化算法,在解决多参数非线性函数的优化问题上具有良好的性能,为了有更好的收敛精度和更快的收敛速度,本文构建了带有压缩因子的粒子群算法,可用于设计反应谱的标定。利用这一方法可给出第一拐点周期、特征周期、平台值和衰减指数等刻画设计反应谱特征的参数值。本文以埃尔森特罗台(El Centro)加速度时程的反应谱标定为例,采用本文提出的改进粒子群算法、Newmark三参数法、双参数法和差分进化算法对其进行标定。对比分析了4种标定方法给出的特征参数及计算精度,实例证明,改进粒子群算法具有较高的精度,给出的设计反应谱较好地反映了地震反应谱的特征。 相似文献
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在地震综合预测投影寻踪研究工作中,投影寻踪回归算法是其中应用最多的一种方法．但一般投影寻踪回归算法构造技术较为复杂,采用多次局部光滑回归,计算量较大,外推较为繁杂,容易陷于局部解．在综合考虑传统投影寻踪回归算法特点的基础上,针对投影寻踪回归计算中存在的一些不利因素,给出了一定的解决思路:采用粒子群优化算法代替高斯 牛顿算法优化投影方向;采用厄米多项式代替分段线性光滑回归来拟合岭函数,以简化优化过程;参数优化无需分组,获得全局优化的岭函数．利用数值仿真技术进行基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型建模能力与计算精度的检验,再将其应用于多维地震时间序列和一般多维无序地震样本回归综合建模预测中．通过计算和分析表明,基于粒子群优化算法与厄米多项式构建的投影寻踪回归模型具有简单、快速、有效的特点,在实际地震综合预测建模中取得了满意的效果,可作为地震预测的一种综合分析方法． 相似文献
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当前对建筑空间结构进行优化时,所采用的算法趋同性高,无法实现多目标种群优化,易陷入局部最优解,存在寻优质量低、优化成本高、抗震性能低的问题。针对上述问题,提出一种基于改进粒子群算法的建筑空间结构优化方法。该方法以空间结构的抗震性能、工程造价为优化目标,来优化建立建筑空间结构设计;引入多子群协同进化机制解决建筑空间结构抗震优化设计中多目标间的种群优化问题,同时引入外部档案和精英学习策略改进粒子群算法,筛选出满足目标函数的最优设计方案,完成抗震性约束的建筑空间结构优化。实验结果表明:所提方法对建筑空间结构优化时的特点为寻优质量高、优化成本低、抗震性能高。 相似文献
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Mohammad Ali Ahmadi Sohrab Zendehboudi Ali Lohi Ali Elkamel Ioannis Chatzis 《Geophysical Prospecting》2013,61(3):582-598
Reservoir characterization involves describing different reservoir properties quantitatively using various techniques in spatial variability. Nevertheless, the entire reservoir cannot be examined directly and there still exist uncertainties associated with the nature of geological data. Such uncertainties can lead to errors in the estimation of the ultimate recoverable oil. To cope with uncertainties, intelligent mathematical techniques to predict the spatial distribution of reservoir properties appear as strong tools. The goal here is to construct a reservoir model with lower uncertainties and realistic assumptions. Permeability is a petrophysical property that relates the amount of fluids in place and their potential for displacement. This fundamental property is a key factor in selecting proper enhanced oil recovery schemes and reservoir management. In this paper, a soft sensor on the basis of a feed‐forward artificial neural network was implemented to forecast permeability of a reservoir. Then, optimization of the neural network‐based soft sensor was performed using a hybrid genetic algorithm and particle swarm optimization method. The proposed genetic method was used for initial weighting of the parameters in the neural network. The developed methodology was examined using real field data. Results from the hybrid method‐based soft sensor were compared with the results obtained from the conventional artificial neural network. A good agreement between the results was observed, which demonstrates the usefulness of the developed hybrid genetic algorithm and particle swarm optimization in prediction of reservoir permeability. 相似文献
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The optimal seismic design of structures requires that time history analyses （THA） be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function （RBF） neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization （BPSO） is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures. 相似文献
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This paper proposes an approach to estimate reliability of a storm water drain (SWD) network in fuzzy framework. It involves: (i) use of proposed fuzzy Monte-Carlo simulation (FMCS) methodology to estimate fuzzy reliability of conduits in the network, (ii) construction of a reliability block diagram (RBD) for the network (system) using suggested guidelines, and (iii) use of the RBD and reliability estimates of the conduits in the network to compute system reliability based on a proposed procedure. In addition, a system reliability based methodology is proposed for design/retrofitting of SWD network by optimization of its conduit dimensions. Conventionally used reliability analysis approaches assume that the cumulative distribution function (CDF) of performance function (marginal safety) of conduits follows Gaussian distribution, which cannot be ensured in the real world scenario. The proposed approach alleviates the need for making such assumptions and can account for linguistic ambiguity in variables defining the performance function. Effectiveness of the proposed approach is demonstrated on a hypothetical SWD network and a real network in Bangalore, India. Comparison of the results obtained from the proposed approach with those from conventional Monte-Carlo simulation (MCS) reliability assessment approach indicated that the estimate of system reliability and conduit reliability are higher with FMCS approach. Consequently, conduit dimensions required to attain required system (network) reliability could be expected to be lower when FMCS approach is used for designing or retrofitting a system. 相似文献