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1.
基于粒子群优化神经网络算法的深基坑变形预测方法 总被引：1，自引：0，他引：1
深基坑变形预测是进行施工参数调整和确保深基坑施工安全的重要手段,而如何对其变形进行有效、准确的预测是一个有待解决的技术难题。采用粒子群优化算法对神经网络模型的初始权值和阈值进行优化,并将已有的变形监测数据作为神经网络的输入参数,建立了基于粒子群优化神经网络算法的深基坑变形预测方法。将形成的方法应用于长春市火车站北广场深基坑开挖监测工程中。结果表明：8号水平位移测点预测结果的均方根误差为3.78%,平均百分比误差为5.48%;9号地面沉降点预测结果的均方根误差为5.62%,平均百分比误差为3.23%。经验证,本文方法预测深基坑开挖过程中的变形具有较高的可信度。 相似文献
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基于粒子群优化算法的小波神经网络缝洞型储层识别模型 总被引：1，自引：1，他引：0
针对缝洞型储层识别精度较低这一难题,提出了基于粒子群优化算法的小波神经网络（PSO—WNN）储层识别模型。以小波函数作为隐含层的激励函数,采用粒子群优化算法,对权值、伸缩参数、平移参数进行调整,构建出基于粒子群优化算法的小波神经网络储层识别模型。该模型具有算法简单、结构稳定、计算收敛速度快、全局寻优能力强、识别精度高、泛化能力强的优点。这里以济阳坳陷桩西埕岛地区古生界潜山缝洞型储层识别为例,利用常规测井参数作为模型的输入参数,以储层类型赋值作为输出,选取九口井的108个已知样本,采用不同隐含层个数对模型进行多次训练。通过对比分析,最终确定隐含层个数为10,建立起该区的Ⅰ类、Ⅱ类、Ⅲ类储层识别模型。利用已建模型对十八个检验样本进行识别,其识别正确率高达100％,而BP神经网络识别正确率为88％。这表明该模型对缝洞型储层的识别效果较好,为缝洞型储层的进一步研究提供了可靠的依据。 相似文献
3.
针对地震勘探资料依赖线性优化方法进行波阻抗反演不易得到全局极值的问题,提出一种改进的粒子群优化算法-自适应粒子群优化算法进行波阻抗反演。自适应粒子群优化算法是以群智能优化理论为基础,通过3种可能移动方向的带权值组合进行全局寻优。该方法搜索速度较快,且具有较强的全局寻优能力。通过函数测试和波阻抗反演的应用,结果表明,自适应粒子群优化算法是一种适应能力较强的全局优化算法,用该方法进行波阻抗反演是可行有效的。 相似文献
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为了分析边坡的稳定性,利用协调粒子群算法和BP网络建立了边坡稳定性CPSO-BP预测模型。BP网络能够很好地描述边坡稳定性与其影响因素之间复杂的非线性关系,将内摩擦角、边坡角、岩石重度、边坡高度、黏聚力、孔隙压力比6个主要影响因素作为网络的输入,将边坡稳定性系数作为网络的输出。为避免BP网络陷入局部最优,利用协调粒子群算法的全局优化能力确定BP网络的连接权值和阀值,使BP网络的优势得到分发挥,达到提高模型预测精度目的。实例表明CPSO-BP模型有更好地预测精度以及将其应用于边坡稳定性预测是可行的。 相似文献
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改进的BP神经网络在流域产沙量预测中的应用 总被引：1，自引：0，他引：1
误差逆传播算法是多层前向网络的典型算法，但是其平方误差函数超曲面存在许多局部极小值，于是给出了基于输出空间的全局优化BP算法（global optimization back propagation algorithm, 简称GOBPA），应用GOBPA，建立黄河某流域年均产沙量的预测模型，结果表明，用GOBPA训练的多层前向神经网络能够以很高精度预报年均产沙量。 相似文献
7.
《物探化探计算技术》2020,(3)
探地雷达反演问题是高度非线性的,采用线性反演方法往往难以获得较好的反演效果,因此提出了将生物地理学优化算法同粒子群优化算法相结合的混合非线性反演方法。将该方法用于探地雷达时间域波形反演,采用时间域有限差分方法进行正演,以信号的均方误差函数作为目标函数,并针对波形反演的特点,在目标函数中加入波形的导数拟合差作为约束项,实现了结构层厚度和介电常数的波形反演。对比经典粒子群算法和生物地理学优化算法在多层介质仿真数据的一维波形反演中的效果,验证了该改进算法的有效性和抗噪性。 相似文献
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基于BP神经网络的滑坡监测多源异构数据融合算法研究 总被引：1，自引：1，他引：0
针对滑坡监测中的多源异构数据融合问题，论文提出了一种基于BP神经网络的多源异构监测数据融合算法。该算法将影响滑坡变形的温度、湿度、风力、云量、单日降水量和累计降水量等多环境因子变量作为输入变量，以滑坡位移变化量数据作为期望输出数据，并利用各环境因子变量和滑坡位移变化量的相关性及显著性进行环境因子变量筛选，以提高算法的预测精度。论文采用甘肃省永靖县黑方台党川滑坡的实测数据进行了试验，结果表明:反向传播（Back-Propagation，BP）神经网络数据融合算法适用于具有多源异构监测数据的滑坡变形预测；在进行环境变量因子筛选后，BP神经网络数据融合算法的决定系数达到0.985，均方根误差（RMSE）达到0.4787 mm，从而有效提高了变形预测结果的精度。 相似文献
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细胞神经网络方法是一种有效的重力异常提取方法,提取出的重力异常信息具有较强的横向分辨能力,但如何训练优化模板是影响该方法提取精度的关键所在。笔者引入粒子群优化算法进行参数优化,结合细胞神经网络和粒子群方法各自的特点,通过细胞神经网络动态性能分析,对模板加入约束条件,并在粒子群算法中引入收缩因子,提出一种基于改进的粒子群优化的细胞神经网络算法。使用该方法进行了模型验证和实际资料处理,处理结果表明,该方法提高了重力异常提取的稳定性,能够更准确地提取油气藏重力异常信息。 相似文献
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将粒子群算法（PSO）引入大坝监测领域,提出一种基于粒子群神经网络（PSONN）的大坝监控预报模型。该模型充分发挥PSO的全局寻优能力和BP神经网络局部细致搜索优势,给BP神经网络提供了良好的初始权值。对逐一粒子群（SPSONN）、整体粒子群（WPSONN）、逐一BP（SBPNN）及整体BP（WBPNN）4种预报模型的对比分析表明：逐一预报模型（SPSONN和SBPNN）的预报精度明显高于对应的整体预报模型（WPSONN和WBPNN）的预报精度;与BP神经网络模型相比,PSONN模型不仅收敛速度明显加快,而且预报精度也有较大提高,尤其是SPSONN模型,其高精度和短历时性完全满足实时预报的需要,可以准确、有效地应用于大坝监测量的实时预报。 相似文献
11.
Storm surge prediction using an artificial neural network model and cluster analysis 总被引：1，自引：1，他引：0
In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with
a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis
methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage
method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate
model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and
the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed
data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied
only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can
be used to predict regional storm surges and may be used to develop a forecast system. 相似文献
12.
导水裂隙带高度是西部矿区保水采煤的理论依据和关键参数。近年来,BP神经网络广泛应用于导水裂隙带高度预测,但BP神经网络存在收敛速度慢、易陷入局部极小等问题。为提高导水裂隙带高度预测的准确性,利用粒子群优化算法(PSO)对BP神经网络的权值和阈值进行优化,建立基于PSO-BP神经网络的导水裂隙带高度预测模型。选择开采厚度、开采深度、工作面倾斜长度、煤层倾角、覆岩结构特征为导水裂隙带高度主要影响因素,选取22例导水裂隙带高度实测数据对PSO-BP神经网络进行训练,将训练后的PSO-BP神经网络对2例测试样本的预测结果与实际值进行对比,并与BP神经网络预测模型及经验公式预测结果进行对比。结果表明:PSO-BP神经网络预测模型的平均相对误差为1.55%;BP神经网络预测模型的平均相对误差为4.8%,经验公式的最小相对误差为9.4%,PSO-BP神经网络预测精度明显优于BP神经网络和经验公式,且绝对误差和相对误差变化较稳定,可以有效预测导水裂隙带高度。 相似文献
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Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications. 相似文献
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基坑工程施工过程中的周边地面沉降直接关系到周围建筑物的安全,本文根据上海前滩地区某基坑工程的历史监测数据、施工工况和周边地层参数等多源数据对基坑周边地面沉降进行监测和预测。以PSO-BP神经网络为基础,通过将基于时序和基于沉降影响因素的网络模型对比发现：二者预测结果误差较小且基于时序的神经网络预测精度更高,说明利用PSO-BP神经网络能够很好地对基坑周边地面沉降进行分析与预测。为了综合考虑时间效应和空间效应的影响,在基于沉降影响因素的预测模型的基础上加入历史监测数据作为模型输入层进行优化,结果表明：优化后的PSO-BP神经网络模型具有更小的相对误差范围和更高的预测精度,在基坑周边地面沉降预测中有很好的应用前景。 相似文献
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Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system 总被引：3，自引：0，他引：3
The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression
model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit
mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance
and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been
made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference
system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing
an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per
delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site,
the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance
indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between
predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison
has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which
has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison
has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based
model. 相似文献
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An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia 总被引：7，自引：2，他引：5
Masoud Bakhtyari Kia Saied Pirasteh Biswajeet Pradhan Ahmad Rodzi Mahmud Wan Nor Azmin Sulaiman Abbas Moradi 《Environmental Earth Sciences》2012,67(1):251-264
Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) techniques are being increasingly used for flood modeling. Previously, this ANN method was frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors using ANN techniques and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN model for this study was developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, remote sensing data, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. To measure the performance of the model, four criteria performances, including a coefficient of determination (R 2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor. 相似文献
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以内蒙古自治区开鲁县玉米作物为研究对象,将生育期内玉米遥感影像所提取的多种植被指数和实地采样点的测产数据作为训练值,利用BP(back propagation)神经网络和遗传算法优化BP(GA-BP)神经网络估产模型,得出网络预测的玉米产量数值。通过决定系数R 2和均方根误差RMSE,比较实测产量与预测产量之间的精度,BP神经网络模型R^2为0.8452,RMSE(%)为28.37;遗传算法优化BP神经网络模型R^2为0.9850,RMSE(%)为6.70,表明遗传算法优化BP神经网络估产模型具有一定可行性和可信度。 相似文献
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为了提高机器学习对深基坑地面沉降的预测能力,本文提出了一种基于Stacking集成学习方式的多模型融合的地面沉降预测方法,并以深圳某深基坑为例,采用斯皮尔曼相关性系数对基坑地面沉降的影响因子进行筛选;运用筛选后的8个影响因子建立Stacking深基坑地面沉降预测模型,以验证该方法的适用性。结果表明：Stacking预测模型的平均绝对误差为0.34、平均绝对误差百分比为2.22%,均方根误差为0.13,相较于传统基模型（随机森林、支持向量机和人工神经网络）,Stacking预测模型的平均绝对误差、平均绝对误差百分比和均方根误差值皆为最小。 相似文献