Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system,which can be excited of harmful vibration by external loads.It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model.In this paper,an adaptive inverse control method is proposed on the basis of novel rough neural networks (RNN) to control the harmful vibration of the offshore jacket platform,and the offshore jacket platform model is established by dynamic stiffness matrix (DSM) method.Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory,RNN is utilized to identify the predictive inverse model of the offshore jacket platform system.Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads,and to deal with the delay problem caused by signal transmission in the control process.The numerical results show that the constructed novel RNN has advantages such as clear structure,fast training speed and strong error-tolerance ability,and the proposed method based on RNN can effectively control the harmfid vibration of the offshore jacket platform.     

风场反演中距离圈上速度退模糊方法研究

在深入分析速度模糊的特点后,根据在距离圈上不模糊点到模糊点的不连续性,以及对模糊跳变处风速差和最大不模糊速度间关系的判定,从零方位开始在距离圈上逐点进行速度模糊的判定,并引入模糊标记的形式以确定退模糊处理的区间.然后通过实例分析,证明了方法的可用性和正确性.最后利用经过该方法处理的低仰角基本速度资料成功反演了雷达风场.     

太湖水质参数MODIS的遥感定量提取方法

本文利用MODIS数据的可见光、近红外波段和准实时的地面采样数据,分别利用线性回归模型和神经网络模型反演了太湖的叶绿素a和悬浮物浓度.结果表明,利用MODIS数据的波段组合(M2/M8)和(M2/M9)可估算太湖的叶绿素a浓度;而MODIS数据的波段组合(M12/M17)、(M13/M17)及MODIS(M4)波段能定量估算太湖的悬浮物浓度,但估算精度仍不能满足实际需要.因此,构建了一个以MODIS可见光及近红外波段为输入,以太湖水质参数为输出的2层BP神经网络模型反演太湖的水质参数,大大提高了反演精度.     

Uncertainty representation of ocean fronts based on fuzzy-rough set theory

Analysis of ocean fronts' uncertainties indicates that they result from indiseemibility of their spatial position and fuzzi-ness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncer-tainties, then they are classified into three sets: with members one hundred pereent belonging to the ocean front, belonging to the ocean front's edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are util-ized to evaluate the ocean front's degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts' uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.     

天津市中心城区地下空间资源评价

阐明了地下空间资源评价的基本概念和原理,分析了影响天津市中心城区地下空间资源开发的自然因素,采用GIS-模糊综合评价模型对地下空间资源进行了评价,通过评价建立了地下空间资源数据库,完成了地下空间资源分布图,科学反映和度量了地下空间可供开发利用的资源,为天津市地下空间综合利用规划编制提供了基础数据和科学依据.     

基于遗传—神经网络方法的广西台风灾害评估模型研究

选取1981—2018年影响广西且灾情记录比较完整的86个台风样本,基于台风灾害伤亡人数、直接经济损失划分灾情等级,选取致灾因子,利用遗传算法与神经网络相结合的方法建立广西台风灾害评估模型.结果表明:选取的台风灾害致灾因子与台风灾情等级之间具有显著的相关性,构建的遗传—神经网络集合预报模型对台风灾情预估效果较好,训练样...     

模糊ISODATA聚类算法在声速剖面自动分类中的应用

依据中国海浅海区30′按月历史统计声速剖面数据,通过归一化处理和Akima差值采样得到梯度剖面,建立起各方区按月归化后的声速剖面分层梯度样本集,并采用模糊ISODATA聚类算法对声速剖面进行聚类分析.通过对分类结果和类内总方差和的分析表明,聚类参数m值在1.1～2.1之间,并以最远邻系统聚类法结果为初始类中心的模糊分类效果较好.应用该方法对海洋中的声速剖面进行自动分类和区划对海洋环境的战术应用意义重大.     

基于纹理分析和人工神经网络的SAR图像中海面溢油识别方法

基于纹理分析和人工神经网络建立了用于区别SAR图像中溢油现象和疑似溢油现象的模型.引入图像处理中的纹理分析作为识别溢油现象的特征参量,并利用方差分析对计算的31个特征参量进行筛选作为神经网络的输入.结果表明,模型能够较好的识别溢油现象,测试样本集的总体精度为0.83;纹理特征作为特征参量以及基于方差分析的特征参量筛选提高了溢油现象的识别精度.     

海洋平台结构损伤识别与定位新方法及试验验证

基于小波变换的时频局域化特性和BP神经网络的非线性映射特性,结合两者优点提出了基于小波包分析和神经网络方法的海洋平台三步法损伤定位方法。对海洋平台结构加速度响应信号进行小波包分析,提取小波包特征向量,将小波包结点能量变化量指标作为BP网络的输入向量,逐步确定损伤位置。设计一典型导管架式海洋平台试验模型,分别进行岸上脉冲激励及水池中波浪激励下平台结构损伤识别与定位模型试验,对该方法的可行性和适用性进行了验证。     

基于模糊聚类的海水水质监测技术研究

海水水质监测数据纷繁复杂,为了在海量的数据中提取有效数据进行分析,采用模糊聚类实现对监测数据的分析处 理。对监测数据分析结构做了简要说明,接着分析模糊聚类C 聚类均值算法（FCM） 不足之处,提出了基于不完整数据样本 的FCM 算法,最后采用海水赤潮数据不完整样本进行实例仿真,实验证明,该算法在处理不完整样本数据时有一定的优势。