共查询到20条相似文献，搜索用时 31 毫秒
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The tracking control problem of AUV in six degrees-of-freedom (DOF) is addressed in this paper. In general, the velocities of the vehicles are very difficult to be accurately measured, which causes full state feedback scheme to be not feasible. Hence, an adaptive output feedback controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed, in which the location information is only needed for controller design. The DRFNN is used to online estimate the dynamic uncertain nonlinear mapping. Compared to the conventional neural network, DRFNN can clearly improve the tracking performance of AUV due to its less inputs and stronger memory features. The restricting condition for the estimation of the external disturbances and network's approximation errors, which is often given in the existing literatures, is broken in this paper. The stability analysis is given by Lyapunov theorem. Simulations illustrate the effectiveness of the proposed control scheme. 相似文献
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AbstractDeep-sea mining (DSM) is an advanced technology. This article is focused on the dynamic analysis of a coupled vessel/riser/equipment system of a DSM based on radial basis function (RBF) neural network approximations while considering vessel dynamic positioning (DP) and active heave compensation (AHC). A coupled model including the production support vessel (PSV), lifting riser, and slurry pump is established containing simulated DP and AHC models. Furthermore, dynamic simulations are implemented to obtain the results of the vessel motions, thruster forces, pump motions and riser tensions. Using optimal Latin hypercube sampling, an RBF neural network approximation model is established, the input includes environmental factors and the output includes the dynamic responses of the pump motion and riser tension. Calculations are performed using RBF network approximations instead of a coupled model. The obtained results show that the PSV wave frequency (WF) motions have significant influence on the dynamic responses of the subsea system. Moreover, the current load affects the compensation effect. The RBF network approximation model can be used to reduce the required calculation time. 相似文献
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针对海上条件下,对于实时定位应用,实时数据流无法下载的情况,文中提出一种基于RBF神经网络的卫星钟差预报算法,给出基函数的中心、方差以及隐含层到输出层的权值的计算方法,采用滑动窗口的方法,用样本数据训练后的网络预测下一个历元的钟差值,依次往后训练网络直到预测完整个时间段,通过实验验证了算法的可用性。短期预报中,GPS预报精度在1 ns以下,BDS和GLONASS在2～3 ns左右;长期预报中,GPS预报精度在几十纳秒左右,而BDS和GLONASS在几百纳秒左右,文中给出了相应的结果分析。 相似文献
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Hennessey G. Leung H. Drosopoulos A. Yip P.C. 《Oceanic Engineering, IEEE Journal of》2001,26(3):358-372
Recently, neural networks have been proposed for radar clutter modeling because of the inherent nonlinearity of clutter signals. This paper performs an analysis of the practicality of using a radial basis function (RBF) neural network to model sea clutter and to detect small target embedded in sea clutter. An experiment using an instrumental quality radar was carried out on the eastcoast of Canada to create a rich sea clutter and small surface target database. This database contains both staring and scanning data under various environmental conditions. Using data-sets with different characteristics, we investigate the effects of quantization error, measurement noise, generalization of the neural net over ranges and sampling rate on the RBF clutter model. Despite these physical limitations, the RBF model was shown to approach an optimal predictive performance. The RBF predictor was also applied to detect various small targets in this database based on the constant false alarm rate (CFAR) principle. This RBF-CFAR detector was demonstrated to be able to detect small floating targets even in rough sea conditions 相似文献
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LabVIEW设计中压力传感器的RBF神经网络温度补偿 总被引：5，自引：0，他引：5
在石油平台注水压力监测系统设计中 ,采用LabVIEW虚拟仪器平台 ,嵌入逼近能力强和收敛速度快的RBF神经网络 ,以人工环境实验数据为样本进行训练 ,实现了压力传感器的智能网络温度补偿。结果显示 ,此方法能够在压力、温度变化较大的恶劣环境下 ,获得很高的补偿精度。 相似文献
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为克服温度对溶解氧传感器的影响,对极谱型溶解氧检测系统的溶解氧电极激励源、高精度信号采样、软件标定和温度补偿等方面进行研究。通过对极谱型溶解氧传感器工作原理进行分析,设计了极谱型溶解氧传感器检测电路;根据溶解氧电极的温度特性,设计了基于NTC（负温度系数）热敏电阻的硬件温度补偿电路,并利用最小二乘法及RBF神经网络构建了软件温度补偿模型。利用饱和蒸馏水进行温度补偿实验,结果表明：经温度补偿后,该溶解氧检测系统的相对误差及采样波动均在1%以内,大大减小了传感器的非线性误差,测量精度和稳定性均可满足应用要求。 相似文献
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基于模糊神经网络(FNN)的赤潮预警预测研究 总被引：1，自引：0，他引：1
为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP 算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通 BP 网络、RBF 网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。 相似文献
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西北太平洋柔鱼中长期预测方法研究 总被引：3，自引：0，他引：3
为了能更好预测西北太平洋柔鱼的资源量, 选择合适的预测方法及开发相应的预测系统颇为重要。利用相关性分析, 筛选出在产卵区显著影响西北太平洋柔鱼资源量的关键网格点, 并采用这些网格点的海表温度、产卵区适宜温度所占面积的比例和单位努力捕获量等数据组织样本, 然后利用线性回归、BP 神经网络、RBF 神经网络和支持向量机等预测方法进行实验。结果表明: 在西北太平洋柔鱼中长期预测中, BP 神经网络要优于其他方法。以相关性分析和BP 神经网络为基础建立的西北太平洋柔鱼资源量预测系统是有效可行的。 相似文献
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计算结构可靠度的RBF神经网络响应面法 总被引：6，自引：0，他引：6
对功能函数不能明确表达的问题进行可靠度分析,常采用响应面法。其中二次多项式响应面法应用较为广泛,采用与此方法相同的思路,提出了RBF神经网络响应面法,并通过算例与常用的BP神经网络响应面法进行了对比分析,该方法在学习速度、迭代次数等方面均优于BP神经网络响应面法。该方法用于大型复杂结构的可靠性分析,可相应提高工作效率和解题质量,具有一定实际应用价值。 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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赤潮预测的人工神经网络方法初步研究 总被引：13，自引：0，他引：13
赤潮是一种由多因素综合作用引发的生态异常现象,具有突发性及非线性等特点。对其进行预测预报一直是海洋科学研究的热点。探讨了应用人工神经网络原理进行赤潮预测的方法,简要介绍了BP和RBF算法的基本原理,用2种算法对不同海域赤潮生物与环境因子之间非线性和不确定性的复杂关系进行学习训练和预测检验,并与传统的统计方法进行了比较。结果表明：人工神经网络方法在模拟和预测方面优于传统的统计回归模型,具有较强的模拟预测能力及实用性,值得进一步探索。 相似文献
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低温柔性管道是海上浮式液化天然气装置系统(FLNG)的核心输运装备。针对低温柔性管道多材料、多层复合的结构设计难点,将其按照不同功能解耦成内衬层、抗拉铠装层以及辅助层三个关键结构层。基于神经网络模型(RBF)、Kriging模型以及响应面模型(RSM)三种建模方法建立了上述三个结构层响应分析的代理模型,并通过模型准确度的比较,发现RBF的误差均最小。在优化设计中,基于遗传算法分别对低温柔性管道上述关键结构层进行多目标优化设计。在内衬层结构的优化中,以质量及弯曲刚度最小为优化目标;在抗拉铠装层结构的优化中,以质量最小及拉伸刚度最大为优化目标;在辅助层结构的优化中,以质量、弯曲刚度及传热速率最小为优化目标。研究工作为低温柔性管道的结构提供了关键设计参数及理性的设计方法。 相似文献
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海洋油污染是各类海洋污染中最常见、分布面积最广且危害程度最大的污染之一。近年来,海洋特别是近海人类活动频繁,且随着海上运输和石油加工业的发展,油田井喷、钻井平台爆炸、船舶碰撞等所造成的溢油事故增多,因而,监测海洋溢油具有重要的经济和社会现实意义。研究采用MatLAB工具,通过图像预处理(图像校正和增强)、特征提取和神经网络识别等方法,对合成孔径雷达(SAR)海洋溢油图像进行处理,最终期望实现半自动区分SAR图像上各类目标,并进行多种神经网络方法效果比较。研究首先对SAR海洋溢油图像进行初步人工识别;然后进行图像预处理(几何校正、滤波处理等)和基于灰度共生矩阵的特征值计算;最后,借助神经网络方法对溢油区域和疑似溢油区域进行分类,输出分类处理后的图像。通过输出图像分析发现,神经网络能对SAR海洋溢油图像中溢油、海水、土地3类目标进行明确分类,且RBF神经网络模型精度高于BP神经网络。本文提出的半自动分类方法不仅能提高SAR图像处理效率,将分类目标扩充有溢油和非溢油扩充到溢油、海水、土地3类,提高图像处理的全面性,同时通过比较RBF和BP神经网络在SAR溢油图像分类上的具体优劣,有着较好实际意义。 相似文献
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作者提出一种应用径向基函数网络 (RBF)的云检测方法。此方法要求晴空海域与有云海域均以一定数量的基函数来表征 ,两种基函数可组成一个径向基函数网络。使用欧空局沿轨道扫描辐射计 (ERS- 1/ ATSR)资料对径向基函数网络在云检测中的性能作了验证 ,重点研究网络结构的复杂度对分类结果的影响 ,并与人眼的目视解译作比较 ,结果表明径向基函数网络在云检测中性能良好。 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