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基于神经网络响应面的结构可靠性分析方法研究 总被引:20,自引:1,他引:19
针对二次多项式响应面法存在的缺点 ,建立了神经网络响应面 ,进而提出了基于神经网络响应面的结构可靠性分析方法。通过多种数值试验表明 ,神经网络响应面可以快速、精确地拟合结构的极限状态函数 ,为结构的系统可靠性分析开辟了新的途径 相似文献
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基于塑性极限分析理论和神经网络响应面的结构系统可靠性分析 总被引:1,自引:1,他引:0
根据多层神经网络映射存在定理,提出了基于神经网络响应面和塑性极限分析理论的结构系统可靠性分析的新方法。数值试验表明,该方法可以快速、高质量地求出结构系统可靠性指标,是一个具有发展前景的新方法。 相似文献
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高频地波雷达是海洋环境监测的重要手段,当前已经实现对海流的业务化观测,但是外部因素常引起海流空间探测的不连续性。为解决此问题,尽量保障区域数据的完整性和准确性,本文将BP神经网络技术与空间插值相结合,建立了海流的BP神经网络插值模型,并进行了针对实测数据的缺失插值仿真,通过与反距离权重法和线性插值法插值结果的对比,分析该模型在区域海流大面积缺失、流速整体较大和流速整体较小3个方面的性能。结果表明,BP神经网络插值模型的海流预测效果明显优于其他两种方法,且在流场数据大范围缺失下也取得了良好的效果。 相似文献
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提出一种基于BP神经网络的结构破损诊断方法,该方法以结构破损前后柔度的变化作为破损诊断网络输入,为了解决由于系统响应样本数据空间分布不均匀对网络收敛速度及网络诊断影响问题,对网络训练样本采用广义空间格点进行了交换,模拟算例及应用实例均表明,本文方法能准确诊断结构破损位置与破坏程度,是一种有效的结构破损诊断方法. 相似文献
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This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-? slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-? slope reliability analyses. 相似文献
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This paper presents an artificial neural network (ANN)-based response surface method that can be used to predict the failure probability of c-φ slopes with spatially variable soil. In this method, the Latin hypercube sampling technique is adopted to generate input datasets for establishing an ANN model; the random finite element method is then utilized to calculate the corresponding output datasets considering the spatial variability of soil properties; and finally, an ANN model is trained to construct the response surface of failure probability and obtain an approximate function that incorporates the relevant variables. The results of the illustrated example indicate that the proposed method provides credible and accurate estimations of failure probability. As a result, the obtained approximate function can be used as an alternative to the specific analysis process in c-φ slope reliability analyses. 相似文献
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As water depth increases, the structural safety and reliability of a system become more and more important and challenging. Therefore, the structural reliability method must be applied in ocean engineering design such as offshore platform design. If the performance function is known in structural reliability analysis, the first-order second-moment method is often used. If the performance function could not be definitely expressed, the response surface method is always used because it has a very clear train of thought and simple progranuning. However, the traditional response surface method fits the response surface of quadratic polynomials where the problem of accuracy could not be solved, because the true limit state surface can be fitted well only in the area near the checking point. In this paper, an intelligent computing method based on the whole response surface is proposed, which can be used for the situation where the performance function could not be definitely expressed in structural reliability analysis. In this method, a response surface of the fuzzy noural network for the whole area should be constructed first, and then the structural reliability can be calculated by the genetic algorithm. In the proposed method, all the sample points for the training network come from the whole area, so the true limit state surface in the whole area can be fitted. Through calculational examples and comparative analysis, it can be known that the proposed method is much better than the traditional response surface method of quadratic polynomials, because, the amount of calculation of finite element analysis is largely reduced, the accuracy of calculation is improved, and the true limit state surface can be fitted very well in the whole area. So, the method proposed in this paper is suitable for engineering application. 相似文献
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An approach based on artificial neural network (ANN) is used to develop predictive relations between hydrodynamic inline force on a vertical cylinder and some effective parameters. The data used to calibrate and validate the ANN models are obtained from an experiment. Multilayer feed-forward neural networks that are trained with the back-propagation algorithm are constructed by use of three design parameters (i.e. wave surface height, horizontal and vertical velocities) as network inputs and the ultimate inline force as the only output. A sensitivity analysis is conducted on the ANN models to investigate the generalization ability (robustness) of the developed models, and predictions from the ANN models are compared to those obtained from Morison equation which is usually used to determine inline force as a computational method. With the existing data, it is found that least square method (LSM) gives less error in determining drag and inertia coefficients of Morison equation. With regard to the predicted results agreeing with calculations achieved from Morison equation that used LSM method, neural network has high efficiency considering its convenience, simplicity and promptitude. The outcome of this study can contribute to reducing the errors in predicting hydrodynamic inline force by use of ANN and to improve the reliability of that in comparison with the more practical state of Morison equation. Therefore, this method can be applied to relevant engineering projects with satisfactory results. 相似文献
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1 .IntroductionTheartificialneuralnetwork(ANN)hasbeenwidelyusedinmanyscientificfieldsinrecentyears .Itisakindofinformationmanagementsystemthatresemblesthehumanbraininworkpattern .Comparedwiththetraditionalmethodsofnumericalsimulation ,ANNhastheadvantagesofrelativein dependenceofphysicalmodel,uniformandsimplewayofrealization ,quicknessofcomputing ,andsoon .Sincethemodelofartificialneuronswasfirstlyintroducedin 1 943,ithasbeendevelopedthroughseveralstages.TheapplicationofANNhadnotbeenpopular… 相似文献
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Application of artificial neural networks in tide-forecasting 总被引:3,自引:0,他引:3
An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input–output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record. 相似文献
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Learning from data for wind-wave forecasting 总被引:1,自引:0,他引:1
Along with existing numerical process models describing the wind-wave interaction, the relatively recent development in the area of machine learning make the so-called data-driven models more and more popular. This paper presents a number of data-driven models for wind-wave process at the Caspian Sea. The problem associated with these models is to forecast significant wave heights for several hours ahead using buoy measurements. Models are based on artificial neural network (ANN) and instance-based learning (IBL) .To capture the wind-wave relationship at measurement sites, these models use the existing past time data describing the phenomenon in question. Three feed-forward ANN models have been built for time horizon of 1, 3 and 6 h with different inputs. The relevant inputs are selected by analyzing the average mutual information (AMI). The inputs consist of priori knowledge of wind and significant wave height. The other six models are based on IBL method for the same forecast horizons. Weighted k-nearest neighbors (k-NN) and locally weighted regression (LWR) with Gaussian kernel were used. In IBL-based models, forecast is made directly by combining instances from the training data that are close (in the input space) to the new incoming input vector. These methods are applied to two sets of data at the Caspian Sea. Experiments show that the ANNs yield slightly better agreement with the measured data than IBL. ANNs can also predict extreme wave conditions better than the other existing methods. 相似文献
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基于EMD与神经网络的机械故障诊断技术 总被引:2,自引:0,他引:2
经验模式分解 (EMD)是分析非线性、非平稳信号的有力工具 ,它将信号分解为突出了原信号的不同时间尺度的局部特征信息的内在模函数 (IMF)分量。本文通过将各 IMF分量输入到 BP网络中进行训练学习和故障诊断 ,比直接输入原信号可以提高 BP网络对故障诊断的准确率 ,而且减少了训练时间。 相似文献
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Risers and anchor lines play important roles in offshore oil exploitation activities nowadays. For this reason the proper analysis and design of such slender structures has been of a paramount interest. The principal characteristics to be accounted for in riser and mooring line analysis are the severe nonlinearities involved and the random dynamic effects associated. The Finite Element Method (FEM) is an essential step to cope with this kind of analysis. But the use of the FEM can be computationally very expensive for the solution of the resultant nonlinear differential equations of motion, because the time-domain integration should produce sufficiently long response time-histories using small time-steps in order to obtain reliable time-series statistics of any structural response parameter, e.g., top tension in an anchor line or stresses occurring at a critical section in a steel catenary riser (SCR). This paper presents a very efficient hybrid Artificial Neural Network (ANN)–Finite Element Method (FEM) procedure to perform a nonlinear mapping of the current and past system excitations (inputs) to produce subsequent system response (output) for the random dynamic analysis of mooring lines and risers. Firstly, a quite short FEM-based time-domain response simulation is generated. Then, an ANN is used to predict the remaining structural response time-history simulation. The hybrid ANN–FEM approach can be very efficient for predicting long response time-histories. It has been observed that a 3 h response time-history can accurately be obtained with approximately the computational cost of a 500 s one, i.e., 20 times faster than a complete simulation using finite element-based solution. Roughly, this can represent a reduction of about a dozen of hours of computer time for a single mooring line analysis and about two dozens of hours (or more) for a single SCR analysis, both belonging to a deep-water floating unit. 相似文献
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Accurate and reliable eutrophication level forecasting models are necessary for characterizing complicated water quality processes in bays. In this study, the ability of coupled discrete wavelet transform (DWT) with artificial neural network (ANN) and multi linear regression (MLR) (WANN and WMLR), ANN, MLR and genetic algorithm-support vector regression (GA-SVR) models for chlorophyll-a level forecasting applications were considered. The data used to develop and validate the models were monthly chlorophyll-a (Chl-a) data recorded from January 1994 to December 2013 were obtained from the NO.36 station located in the South San Francisco bay, USA. In the proposed WANN and WMLR models, the observed time series of Chl-a were decomposed to sub time series at different scales by DWT. Afterwards, the sub time series were used as input data to the ANN and MLR systems to predict the 1 month ahead Chl-a. Also the genetic algorithm was linked to SVR models to search for the optimal SVR parameters. The relative performance of the proposed models was compared together and the results showed that the WANN models were found to provide more accurate monthly Chl-a forecasts compared to the other models. The determination coefficient was 0.87, −0.04, 0.31, −2.36 and 0.24 for the WANN, WMLR, ANN, MLR and GA-SVR models, respectively. In addition, the WANN model predicted extreme Chl-a values precisely. The results indicate that the WANN models are a promising new method for eutrophication level forecasting in bays such as those found in South San Francisco Bay. 相似文献