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基于遗传算法和模糊神经网络的边坡稳定性评价 总被引：4，自引：0，他引：4
边坡工程是一个动态的、模糊的、开放的复杂非线性系统,传统的分析方法有时难以对复杂边坡的稳定性做出符合实际的评价。影响边坡稳定性的因素复杂且具有随机性和模糊性。由于神经网络方法不仅能考虑定量因素,而且能考虑定性因素的影响,因而神经网络方法适用于解决非确定性的边坡稳定性评价问题。综合考虑影响边坡稳定性的各方面因素,建立了基于遗传算法的模糊神经网络模型,并利用大量工程资料对网络进行训练和测试。预测结果表明,该模型的预测精度明显高于目前同类方法。 相似文献
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The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Hölder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Hölder exponents of the data set 1 and the Hölder exponents of the radioactive elements concentrations; data set 5: the estimated Hölder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Hölder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data. 相似文献
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水下爆破是一个复杂的、非线性的动态能量释放过程,其涉及到的影响因素众多。为了充分利用少量的实测数据,较准确地预测水下爆破质点峰值振动速度,引入灰色关联分析理论,并结合遗传神经网络较强的非线性映射优势和全局化的搜索能力,建立基于灰色关联分析的遗传神经网络模型(GRA-GA-BP)。该模型利用灰色关联分析理论,充分挖掘小样本潜在信息特征,较合理地确定了影响爆破振动速度的主要因素,解决了神经网络在多变量复杂系统中输入变量无法自动寻优的难题,从而增强了神经网络的适应能力和稳定性。采用该模型对广东台山核电站1期工程大襟岛水下爆破开挖质点峰值振动速度进行预测,并与传统的遗传神经网络及萨道夫斯基公式预测结果进行对比,发现GRA-GA-BP模型的预测值与实测值吻合更好,预测误差更稳定。研究方法可为小样本、多因素影响下类似工程质点峰值振动速度预测提供借鉴。 相似文献
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This study deals with reservoir characterization based on well log data using an unsupervised self-organizing map (SOM) and supervised neural network algorithms with the aim of clustering log responses into reservoir facies of an oil field located in southwest of Iran. In order to promote and justify the quality control and quantify spatial relationships for petrophysical properties, some of neural network-based approaches were introduced such as the SOMs as the intelligent clustering method compared with other hybrid methods, principal component analysis networks (PCANs) and multilayer perceptron (MLP) and statistical clustering (CA) methods. The results obtained from all the abovementioned methods are compared to each other, and the best option is selected based on accuracy and capabilities of clustering and estimation of the petrophysical data, concluding that for predicting any characteristic of the reservoirs, the appropriate network should be chosen and a unique network cannot be convenient for all of them. Accordingly, the SOM clustering technique was employed to classify the reservoir rocks. Based on the SOM visualization, the reservoir rocks were classified into six facies associated with specific petrophysical properties; among them, F6 expressed the best reservoir quality which is characterized by the low amount of density, highest DT, high amount of neutron porosity (NPHI), and lowest GR response. Ultimately, the performance of all the methods was compared to estimate the porosity and permeability within each facies. The results revealed the preference and reliability of PCAN in predicting porosity and confirmed the capability of MLP in permeability prediction. This study also indicates that neuro-prediction of formation properties using well log data is a feasible methodology for optimization of exploration programs and reduction of expenditure by delineating potentially oil-bearing strata with higher accuracy and lower expenses. The resulting neural net-based model can be used as a powerful and distributive system to reduce the high impact of risk in similar fields. 相似文献
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作者借用线性代数方程组定解讨论中的相关概念，类比表述了前人工神经网络的定解和泛化问题，同时说明解决泛化问题仅公给定网络VC维是不够的，还需要研究本集规模、向量维度、相关性以及模拟对象的复杂度，文章出相应的算例，一定程度上讨论了前向神经网络的演进特征。 相似文献
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In geosciences, complex forward problems met in geophysics, petroleum system analysis, and reservoir engineering problems often require replacing these forward problems by proxies, and these proxies are used for optimizations problems. For instance, history matching of observed field data requires a so large number of reservoir simulation runs (especially when using geostatistical geological models) that it is often impossible to use the full reservoir simulator. Therefore, several techniques have been proposed to mimic the reservoir simulations using proxies. Due to the use of experimental approach, most authors propose to use second-order polynomials. In this paper, we demonstrate that (1) neural networks can also be second-order polynomials. Therefore, the use of a neural network as a proxy is much more flexible and adaptable to the nonlinearity of the problem to be solved; (2) first-order and second-order derivatives of the neural network can be obtained providing gradients and Hessian for optimizers. For inverse problems met in seismic inversion, well by well production data, optimal well locations, source rock generation, etc., most of the time, gradient methods are used for finding an optimal solution. The paper will describe how to calculate these gradients from a neural network built as a proxy. When needed, the Hessian can also be obtained from the neural network approach. On a real case study, the ability of neural networks to reproduce complex phenomena (water cuts, production rates, etc.) is shown. Comparisons with second polynomials (and kriging methods) will be done demonstrating the superiority of the neural network approach as soon as nonlinearity behaviors are present in the responses of the simulator. The gradients and the Hessian of the neural network will be compared to those of the real response function. 相似文献
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A MATLAB based backpropagation neural network (BPNN) model has been developed. Two major geo-engineering applications, namely, earth slope movement and ground movement around tunnels, are identified. Data obtained from case studies are used to train and test the developed model and the ground movement is predicted with the help of input variables that have direct physical significance. A new approach is adopted by introducing an infiltration coefficient in the network architecture apart from antecedent rainfall, slope profile, groundwater level and strength parameters to predict the slope movement. The input variables for settlement around underground excavations are taken from literature. The neural network models demonstrate a promising result predicting fairly successfully the ground behavior in both cases. If input variables influencing output goals are clearly identified and if a decent number of quality data are available, backpropagation neural network can be successfully applied as mapping and prediction tools in geotechnical investigations. 相似文献
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地表移动预计参数选取的神经网络法 总被引：6，自引：0，他引：6
地表移动预计参数的选取是研究地表移动及其规律的重要内容，由于预计参数受多种复杂因素的影响，具有高度的不确定性和离散性，利用神经网络具有自组织、自学习和高度非线性映射的能力，并既能考虑定量因素又能考虑定性因素的优点，可建立地表移动预计参数选取的神经网络模型以及对BP神经网络进行改进。利用大量的地表移动实际观测数据样本对该网络模型进行训练和学习，并用该网络模型对地表移动参数进行预计，结果表明，该改进的BP神经网络具有收敛速度快、预计参数精度高的优点，从而为开采沉陷地表移动预计中参数的选取提供了新方法。 相似文献
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应用前馈人工神经网络对广域单调的两组样本进行了模拟反演，引入单调前馈网络的概念对其权值和阈值定解问题和泛化能力进行了较诉研究。表明前馈人工神经网络是一个表达形式简单的复杂系统，其单调特征是隐性的，而且训练网络的成熟性对样本数量和样本内在规律性有一定依赖。强调了前馈人工神经网络的应用效果，指出单调与复合问题还需进一步深入研究。 相似文献
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Building a model to rapidly simulate the impact of typhoons on agriculture and to predict agricultural losses is crucial and great help for remedial measure and distributing subvention right after the disaster. The relationship between typhoon-related meteorological factors and agricultural losses was first evaluated, and the Pearson??s test was applied to find consequences of both landfall and non-landfall which can be appropriately used to synthesize the possible coverage to suitably describe how typhoons influence agricultural losses. The self-organizing feature map (SOM) was then used to map similar properties of data into the same cluster and display the distribution of input?Coutput patterns. Then, the clusters were adopted as centroids of radial basis function (RBF) neural networks. Finally, two hybrid self-organizing radial basis (SORB) networks that integrated SOM into RBF were constructed for predicting the event-based agricultural losses by feeding two different meteorological inputs (scenarios 1 and 2). The results indicate that the constructed SORB network has great ability to capture the relationship between meteorological characteristics and agricultural losses. Previously, it always takes several days to investigate and evaluate the agricultural damages after typhoons, which is a time-consuming process. In this study, the proposed agri-economic model also demonstrates its outstanding predictability, in real-time, and therefore effectively accelerates the official decision making on agricultural compensation after a typhoon strike. 相似文献
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Michael J. Friedel 《Hydrogeology Journal》2016,24(8):2103-2122
Numerical models provide a way to evaluate groundwater systems, but determining the hydrostratigraphic units (HSUs) used in constructing these models remains subjective, nonunique, and uncertain. A three-step machine-learning approach is proposed in which fusion, estimation, and clustering operations are performed on different data sets to arrive at HSUs at different scales. In step one, data fusion is performed by training a self-organizing map (SOM) with sparse borehole hydrogeologic (lithology, hydraulic conductivity, aqueous field parameters, dissolved constituents) and geophysical (gamma, spontaneous potential, and resistivity) measurements. Estimation is handled by iterative least-squares minimization of the SOM quantization and topographical errors. Application of the Davies-Bouldin criteria to k-means clustering of SOM nodes is used to determine the number and location of discontinuous borehole HSUs with low lateral density (based on borehole spacing at 100 s m) and high vertical density (based on cm-scale logging). In step two, a scaling network is trained using the estimated borehole HSUs, airborne electromagnetic measurements, and numerically inverted resistivity profiles. In step three, independent airborne electromagnetic measurements are applied to the scaling network, and the estimation performed to arrive at a set of continuous HSUs with high lateral density (based on sounding locations at meter (m) spacing) and medium vertical density (based on m-layer modeled structure). Performance metrics are used to evaluate each step of the approach. Efficacy of the proposed approach is demonstrated to map local-to-regional scale HSUs using hydrogeophysical data collected at a heterogeneous surficial aquifer in northwestern Nebraska, USA. 相似文献
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随着土地开发建设规模不断扩大,土地利用情况也在逐年发生变化,准确预测未来土地利用的发展趋势,可以为本地区的土地利用规划提供依据,提升本地区的土地利用效率。传统方法一般采用CA_Markov、ANN以及CA_ANN模型进行预测,存在训练时间长、预测精度不足和缺乏说服力等问题。本文针对上述问题,结合元胞自动机以及人工神经网络模型,建立一种自适应可变滤镜网络模型,针对特定大小区域内的土地类别数目,创建多类数据集来训练不同参数的多个神经网络,可以成功预测未来土地变化的情况,这样就避免了训练单一网络时数据对网络权值的抵消。相比于传统模型中效果最好的CA_ANN模型,本文建立的自适应可变滤镜网络模型不仅总体精度提高了1%~3%,各种地类转化精度提高了12.82%~33.33%,模型预测时间也缩减了49.47%。 相似文献
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Ivica Vilibić Hrvoje Kalinić Hrvoje Mihanović Simone Cosoli Martina Tudor Nedjeljka žagar Blaž Jesenko 《Computational Geosciences》2016,20(1):115-131
We performed a number of sensitivity experiments by applying a mapping technique, self-organizing maps (SOM) method, to the surface current data measured by high-frequency (HF) radars in the northern Adriatic and surface winds modelled by two state-of-the-art mesoscale meteorological models, the Aladin (Aire Limitée Adaptation Dynamique Développement InterNational) and the Weather and Research Forecasting models. Surface current data used for the SOM training were collected during a period in which radar coverage was the highest: between February and November 2008. Different pre-processing techniques, such as removal of tides and low-pass filtering, were applied to the data in order to test the sensitivity of characteristic patterns and the connectivity between different SOM solutions. Topographic error did not exceed 15 %, indicating the applicability of the SOM method to the data. The largest difference has been obtained when comparing SOM patterns originating from unprocessed and low-pass filtered data. Introduction of modelled winds in joint SOM analyses stabilized the solutions, while sensitivity to wind forcing coming from the two different meteorological models was found to be small. Such a low sensitivity is considered to be favourable for creation of an operational ocean forecasting system based on neural networks, HF radar measurements and numerical weather prediction mesoscale models. 相似文献
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H. Aghamohammadi M. S. Mesgari A. Mansourian D. Molaei 《International Journal of Environmental Science and Technology》2013,10(5):931-939
In Iran, earthquakes cause enormous damage to the people and economy. If there is a proper estimation of human losses in an earthquake disaster, it could be appropriately responded and its impacts and losses will be decreased. Neural networks can be trained to solve problems involving imprecise and highly complex nonlinear data. Based on the different earthquake scenarios and diverse kind of constructions, it is difficult to estimate the number of injured people. With respect to neural network’s capabilities, this paper describes a back propagation neural network method for modeling and estimating the severity and distribution of human loss as a function of building damage in the earthquake disaster. Bam earthquake data in 2003 were used to train this neural network. The final results demonstrate that this neural network model can reveal much more accurate estimation of fatalities and injuries for different earthquakes in Iran and it can provide the necessary information required to develop realistic mitigation policies, especially in rescue operation. 相似文献
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淮北煤田五沟煤矿位于童亭背斜西翼中段,为一向斜为主的复式褶皱构造组合,向斜的轴部呈反S形且被断层切割。在系统分析煤矿地质资料的基础上,结合区域构造演化,探讨了构造发育特征、构造演化历史及其形成机制。结果认为:①五沟矿区可以划分为北部断裂构造复杂区、中部褶皱叠加区和南部构造复杂区3个构造分区,断裂构造的组合形式主要有"入"字型、"Y"型、堑垒式及阶梯式。②矿区构造的形成及演化受区域构造控制明显,印支期NS向构造挤压作用形成了近EW向的五沟断层,末期形成五沟复向斜构造;燕山期NW—SE向强烈的挤压作用形成了区内最为显著的NE向断裂及褶皱构造;喜马拉雅期的构造伸展作用造成拉张性构造发育,同时也使区内大、中型主控断裂转变为正断层性质。 相似文献