共查询到19条相似文献,搜索用时 109 毫秒
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为了提高大坝安全监控模型的预测精度并检验模型的泛化能力,研究大坝安全监控的统计模型、BP神经网络模型及遗传神经网络模型,并提出基于这两种神经网络的融合模型,结合某拱坝长期的变形观测数据,对上述几种模型进行试算。分析结果表明,所建立的融合模型与其他模型相比具有较高的预测精度,且泛化能力较强,具有良好的适用性。 相似文献
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近年来,国内外学者在神经网络方面做了大量研究,使神经网络技术在计算、分析、仿真、控制等方面得到广泛应用,在变形监测和测绘数据处理领域,学者们做了大量实验和实践研究,得到丰富的研究和应用成果。本文首先对大坝变形影响因子进行分析,采用主成分分析法提取影响大坝变形的因子元素,最大程度降低因子之间的相关性对神经网络模型的影响。采用改进BP神经网和径向基函数神经网络两种方法,分析大坝变形预测预报效果,并结合相关文献研究成果,对比两种算法的优缺点,探讨神经网络应用于大坝变形监测的可行性。最后结合工程实际应用实例,研究计算表明,改进BP神经网络和径向基函数神经网络都能对实测数据有较好的拟合效果,达到大坝变形预测预报精度,在大坝安全预测预报分析中具有一定的参考和实用价值。 相似文献
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利用神经网络方法评价大坝安全具有一定的优势,但传统大坝安全评价方法不能为神经网络模型提供合适的学习样本。文中引入安全度值的概念,为神经网络提供可量化的学习样本,并针对BP神经网络收敛速度慢、稳定性差、易陷入局部极小等问题,利用遗传算法进行改进,提出基于遗传神经网络的大坝安全评价方法。工程实例表明,评价方法合理、可行。 相似文献
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为了提高大坝变形数据的预测精度,该文提出一种变分模态分解和长短时记忆神经网络相组合的预测模型.对大坝的历史变形数据进行变分模态分解,利用长短时记忆神经网络进行预测,累加各模态分量的预测值完成重构.以江西省某蓄能水电厂2010-2014年大坝监测数据为例,设置不同的对比实验验证VMI-LSTM组合模型的有效性和稳定性.研究表明:组合模型能够有效减小单一模型的误差,VMD能够将变形序列分解为不同频带的分量,减少非线性、非平稳性对预测精度的干扰,且VMD-LSTM神经网络的预测精度要优于其他模型,对于大幅提升大坝变形的预测精度有一定的参考价值. 相似文献
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BP神经网络在大坝监测资料分析处理中有广泛的应用,本文针对改进的BP神经网络:LM-BP网络模型的不足,采用遗传算法加以改进,建立了一种基于遗传算法和LM-BP网络模型的大坝3维变形预报模型GA-LMBP网络模型。将GA-LMBP网络模型应用于小浪底大坝3维变形预报分析中,取得了很好的预报效果,证明了GA-LMBP网络模型是一种行之有效的大坝变形分析预报模型。 相似文献
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以GNSS自动化监测系统的大坝变形预测方法为主要研究目的,针对大坝GNSS自动化监测数据大样本、高采样率、连续等特点,提出了一种结合小波分析与BP、NAR神经网络预测大坝变形的新方法。利用多尺度小波分析对GNSS大坝变形数据序列进行分解与重构,对重构后的低频近似序列采用BP神经网络进行建模预测,对重构后的高频细节序列采取NAR动态神经网络进行建模预测,最后叠加各尺度下预测结果获得大坝变形预测值。应用结果表明,该方法预测精度高、泛化性能好,可广泛应用于采用GNSS自动化监测系统的大坝变形预测。 相似文献
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神经网络算法一直是国内外研究的热点问题,BP神经网络算法具有更小的模型误差,因此,被广泛应用于GPS高程拟合。本文通过对同一区域GPS高程拟合的应用探究,运用迭代运算对比BP神经网络算法与多项式拟合数据,从而证明BP神经网络在一定条件下具有更高的精度,更加突出了BP神经网络算法的实用性。 相似文献
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Targeting the multicollinearity problem in dam statistical model and error perturbations resulting from the monitoring process,we built a regularized regression model using Truncated Singular Value Decomposition(TSVD).An earth-rock dam in China is presented and discussed as an example.The analysis consists of three steps:multicollinearity detection,regularization pa-rameter selection,and crack opening modeling and forecasting.Generalized Cross-Validation(GCV) function and L-curve criterion are both adopted in the regularization parameter selection.Partial Least-Squares Regression(PLSR) and stepwise regression are also included for comparison.The result indicates the TSVD can promisingly solve the multicollinearity problem of dam regression models.However,no general rules are available to make a decision when TSVD is superior to stepwise regression and PLSR due to the regularization parameter-choice problem.Both fitting accuracy and coefficients’ reasonability should be considered when evaluating the model reliability. 相似文献
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基于BP神经网络建立尾矿坝沉降预报模型,重点对BP神经网络的拓扑结构和学习算法进行研究。并以某尾矿库初期坝的沉降监测数据为例,对模型的拟合、预测精度进行验证。实例表明,BP神经网络自学习、自组织能力强,具有极强的线性逼真能力,能够准确地反映输入、输出变量之间的非线性关系,有效地表征尾矿坝的沉降变形规律,对即将发生的变形情况做出科学、合理的预报。 相似文献
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为了使大坝变形的预测精度更高,针对大坝形变量的时间序列中存在着非平稳和非线性等曲线特性,使用一种经验模态分解(EMD)和非线性自回归动态神经网络(NAR)相结合的EMD-NAR模型对大坝形变时间序列进行预测。以某大坝实测的时间序列数据为算例,分别使用BP模型、NAR模型和EMD-NAR模型进行实验对比,结果表明,BP、NAR、EMD-NAR模型预测的均方根误差(RMSE)分别为0.9449,0.6993,0.4678;模型预测的平均相对误差(MRE)分别为0.1492,0.1065和0.0688,从三种模型预测结果对比可知,组合的EMD-NAR模型预测精度最高且稳定性最好,为时间序列的大坝形变预测提供一种新的参考思路。 相似文献
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Xiaoli Wang Xiaoli Zhang Guomin Zhou 《Journal of the Indian Society of Remote Sensing》2017,45(5):785-794
The rice disease is one of the most serious injurious factors that cause major loss of rice production and subsequent economy in agricultural industry. This study explored a new method for obtaining information of the rice disease in a short term through model regression methods. The spectrum characteristics of rice leaves under different disease damage were firstly analyzed for its relationship with rice disease level. The sensitive bands of the spectrum for accurately supervising rice diseases were selected with principal component analysis (PCA). The stepwise regression method and BP neural network were both used to establish the spectrum-based models for recognizing rice diseases. Results showed that five major characteristic bands were determined by PCA (990, 1850, 660, 1921, and 1933 nm) for monitoring foliar rice diseases, among which the edge area for red light had the best correlation with rice disease level was also selected as the parameter to establish the model. Specifically, the composite reflectivity of wavelengths between 990 and 1933 nm was negatively related to rice brown spot diseases stress, which was then used to establish the model. Parameters of the red edge area and the ranged reflectivity between 660 and 990 nm were used to establish models for monitoring rice sheath blight diseases. Totally, there were 60 samples employed to build models for identifying the two diseases by the stepwise regression method and the BP neural network method, and the rest 41 ones were used for further model verification. Compared with the stepwise regression analysis, BP neural network was evaluated to perform better with characteristic bands at 660, 990, and 1933 nm. In conclusion, the establishment of the function model in our study can be implemented to monitor rice diseases, which provided a theoretical basis for indirect and rapid monitoring rice diseases. 相似文献