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Headland-bay beaches are a typical feature of many of the world's coastlines. Their curved planform has aroused much interest since the early days of Coastal Engineering. Modelling this characteristic planform is a task of great interest, not least in relation to projects of coastal structures whose effects on the shoreline must be studied from the planning stages. In this work, Artificial Intelligence is applied to this task—in particular, artificial neural networks (ANNs). Unlike conventional planform models, they are not based on a given mathematical expression of the shoreline curve. Instead, they learn from experience (from a number of training cases) how the planform of a headland-bay beach is shaped, with due regard to the obliquity of incident waves. Three artificial neural networks, with different input/output structures, are implemented and subsequently trained with a number of bays. Once trained, they are tested for validation on other headland-bay beaches. Finally, the most performing neural network is compared with a state-of-the-art planform model. 相似文献
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本文以一种新型激光扫描３－Ｄ视觉传播系统中快速求解３－Ｄ坐标值等为例子,展示ＢＰ神经网络的并行分布处理在本质上具有高速度优势及硬件容错能力;以及如何实现基于模拟神经网络的二进制数字量映射的无误差操作：进而阐明用模拟ＶＬＳＩ技术实现训练后神经网络芯片的可行性和推广意义。 相似文献
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G. Iglesias J. Rabual M.A. Losada H. Pachn A. Castro R. Carballo 《Ocean Engineering》2008,35(11-12):1113-1120
The prediction of rubble-mound breakwater damage under wave action has usually relied on costly and time-consuming physical model tests. In this work, artificial neural networks (ANNs) are applied to estimate the outcome of a physical model throughout an experimental campaign comprising of 127 stability tests. In order to choose the network best suited to the problem data, five different activation function options and 38 network architectures are compared. The good agreement found between the physical model and the neural network shows that an ANN may well serve as a virtual laboratory, reducing the number of physical model tests necessary for a project. 相似文献
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针对水下机器人操纵性优化设计中水动力系数预报问题,在水下机器人水动力预报中引入艇体肥瘦指数概念,确定了水下机器人艇体几何描述的五参数模型。提出采用小波神经网络方法预报水下机器人水动力,确定了神经网络的结构,利用均匀试验设计方法,设计了神经网络的学习样本。研究结果表明,只要确定适当的输入参数,选择适当的学习样本和网络结构,利用小波神经网络方法对水下机器人水动力进行预报可以达到较好的精度。 相似文献
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感潮水闸流量的准确计算对于河网地区水闸引排水效益的分析和水闸综合管理体系的建立具有非常重要的意义。对感潮水闸的水力特性进行了详细分析,认为感潮水闸具有瞬时性和非线性等水力特点,提出采用人工神经网络理论建立其过流量的计算模型。建立了三种计算模式,应用浦东新区东沟水闸资料对不同模式进行了训练、测试和比较,推荐以水闸内外河水位、闸门开启度和上一时刻流量作为神经网络输入的计算模型为最终感潮水闸流量计算模型。研究表明,人工神经网络可以较好地隐含识别感潮水闸的多种出流类型,并具有较强的泛化和容错能力,从而为感潮河网地区水闸流量的计算提供了一种新的解决途径。 相似文献
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The height of a wave at the time of its breaking, as well as the depth of water in which it breaks, are the two basic parameters that are required as input in design exercises involving wave breaking. Currently the designers obtain these values with the help of graphical procedures and empirical equations. An alternative to this in the form of a neural network is presented in this paper. The networks were trained by combining the existing deterministic relations with a random component. The trained network was validated with the help of fresh laboratory observations. The validation results confirmed usefulness of the neural network approach for this application. The predicted breaking height and water depth were more accurate than those obtained traditionally through empirical schemes. Introduction of a random component in network training was found to yield better forecasts in some validation cases. 相似文献
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基于粗糙集与人工神经网络的变压器故障诊断 总被引：2，自引：0，他引：2
根据电力变压器故障诊断问题,提出了基于粗糙集与人工神经网络的变压器故障诊断模型,分析了该模型的实现步骤.采用Kohonen网络对连续属性值进行离散化,应用粗糙集理论对特征参数进行属性约简,并把约简结果生成规则作为BP网络的输入.仿真结果表明,把经过粗糙集理论预处理过的数据送入BP网络训练,提高了学习速度和故障诊断正确率,减少了训练时间. 相似文献
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This paper presents a Recursive Neural Network (RNN) manoeuvring simulation model for surface ships. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. This model is used to test the capabilities of artificial neural networks in manoeuvring simulation of ships. Two manoeuvres are simulated: tactical circles and zigzags. The results between both simulations are compared in order to analyse the accuracy of the RNN. The simulations are performed for the Mariner hull. The data generated to train the network are obtained from a manoeuvrability model performing the simulation of different manoeuvring tests. The RNN proved to be a robust and accurate tool for manoeuvring simulation. 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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Neural networks for wave forecasting 总被引：1，自引：0，他引：1
The physical process of generation of waves by wind is extremely complex, uncertain and not yet fully understood. Despite a variety of deterministic models presented to predict the heights and periods of waves from the characteristics of the generating wind, a large scope still exists to improve on the existing models or to provide alternatives to them. This paper explores the possibility of employing the relatively recent technique of neural networks for this purpose. A simple 3-layered feed forward type of network is developed to obtain the output of significant wave heights and average wave periods from the input of generating wind speeds. The network is trained with different algorithms and using three sets of data. The results show that an appropriately trained network could provide satisfactory results in open wider areas, in deep water and also when the sampling and prediction interval is large, such as a week. A proper choice of training patterns is found to be crucial in achieving adequate training. 相似文献
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Accessible high-quality observation datasets and proper modeling process are critically required to accurately predict sea level rise in coastal areas. This study focuses on developing and validating a combined least squares-neural network approach applicable to the short-term prediction of sea level variations in the Yellow Sea, where the periodic terms and linear trend of sea level change are fitted and extrapolated using the least squares model, while the prediction of the residual terms is performed by several different types of artificial neural networks. The input and output data used are the sea level anomalies (SLA) time series in the Yellow Sea from 1993 to 2016 derived from ERS-1/2, Topex/Poseidon, Jason-1/2, and Envisat satellite altimetry missions. Tests of different neural network architectures and learning algorithms are performed to assess their applicability for predicting the residuals of SLA time series. Different neural networks satisfactorily provide reliable results and the root mean square errors of the predictions from the proposed combined approach are less than 2?cm and correlation coefficients between the observed and predicted SLA are up to 0.87. Results prove the reliability of the combined least squares-neural network approach on the short-term prediction of sea level variability close to the coast. 相似文献
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小波神经网络研究进展及展望 总被引：4，自引：0，他引：4
关于小波分析与人工神经网络结合的研究,近些年来已成为信号处理学科的热点之一,已有大量的研究成果见诸各种学术刊物和会议论文。小波变换具有良好的时频局部性质,神经网络则具有自学习功能和良好的容错能力,小波神经网络（ＷＮＮ）由于较好地结合了两者的优点而具有强大的优势。作者较系统地综述了小波神经网络的研究进展,讨论了小波神经网络的主要模型和算法,并就其存在的一些问题,应用与发展趋势进行了探讨 相似文献
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In this study,an advanced probabilistic neural network(APNN)method is proposed to reflect the global probability density function(PDF)by summing up the heterogeneous local PDF which is automatically determined in the individual standard deviation of variables.The APNN is applied to predict the stability number of armor blocks of breakwaters using the experimental data of van der Meer,and the estimated results of the APNN are compared with those of an empirical formula and a previous artificial neural network(ANN)model.The APNN shows better results in predicting the stability number of armor blocks of breakwater and it provided the promising probabilistic viewpoints by using the individual standard deviation in a variable. 相似文献
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1 .IntroductionAshiptravelingatseaundergoesundesirablewave inducedmotions ,namely ,surge ,sway ,heav ing ,rolling ,pitchingandyaw .Thesemotionsoftencauseproblemstothecrew ,theonboardequip mentand ,intheworstcase ,thesafetyofthevessel.Tominimizethewave inducedshipmotions ,controlsystemsmaybeapplied .Theaccuratemodelingofshipmotionsisthereforeveryimportantforshipdesignanddesignofmotioncontrolsystems .Manyresearchershavedevelopedshipmotionpredictionmethodsbasedonthepotentialflowtheo ries (Dong ,… 相似文献
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1 .IntroductionWiththedevelopmentofoceantechnology ,moreandmoreextremelylargeandlongflexibleoff shoreplatformsusedforoilexplorationanddrillingoperationarebuiltinhostileoceanenvironments .Ingeneral,thiskindofplatformsisanonlineardistributedparametersystemanditsnaturalfrequencyfallsclosertothedominantwavefrequencieswiththeincreaseofwaterdepth .Besides ,itsstructureisverycomplexandtheexternalwaveforceontheplatformisuncertain .Thus ,theseplatformsarepronetoexcessivewave inducedoscillationsunderbot… 相似文献
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基于多种神经网络的风暴潮增水预测方法的比较分析 总被引：1，自引：0，他引：1
简要介绍了利用BP神经网络、小波神经网络、递归神经网络进行风暴潮增水值预测的原理。选取广东省珠江口以南的阳江站2017年风暴潮增水数据进行测试。结果表明,三种神经网络方法针对阳江地区风暴潮增水的预测均具有可靠性和实用性。以当前增水值为输入量的单因子模型更能反映真实风暴潮增水趋势,而从增水极值预测的准确性来看,以台风风力、气压、风向等相关参数为输入量的多因子模型优于单因子模型。BP神经网络更适用于多因子长时间预测,小波神经网络在单因子短时间预测上准确性更高,递归神经网络预测值与实测值相关性更强。在工程运用中,需根据地域时空特点、数据资料的丰富度与预测值评估指标选择合适的方法。 相似文献