Modeling of Multi-Freedom Ship Motions in Irregular Waves with Fuzzy Neural Networks

1 .IntroductionAshiptravelingatseaundergoesundesirablewave inducedmotions ,namely ,surge ,sway ,heav ing ,rolling ,pitchingandyaw .Thesemotionsoftencauseproblemstothecrew ,theonboardequip mentand ,intheworstcase ,thesafetyofthevessel.Tominimizethewave inducedshipmotions ,controlsystemsmaybeapplied .Theaccuratemodelingofshipmotionsisthereforeveryimportantforshipdesignanddesignofmotioncontrolsystems .Manyresearchershavedevelopedshipmotionpredictionmethodsbasedonthepotentialflowtheo ries (Dong ,…     

Neural Network-Based Active Control for Offshore Platforms

1 .IntroductionWiththedevelopmentofoceantechnology ,moreandmoreextremelylargeandlongflexibleoff shoreplatformsusedforoilexplorationanddrillingoperationarebuiltinhostileoceanenvironments .Ingeneral,thiskindofplatformsisanonlineardistributedparametersystemanditsnaturalfrequencyfallsclosertothedominantwavefrequencieswiththeincreaseofwaterdepth .Besides ,itsstructureisverycomplexandtheexternalwaveforceontheplatformisuncertain .Thus ,theseplatformsarepronetoexcessivewave inducedoscillationsunderbot…     

地形坡度对多金属结核分布的控制作用

选取我国东太平洋多金属结核开辟区内的一个区域作为研究区,利用人工神经网络中应用最为广泛的BP网络,建立控制多金属结核分布的地质因素与多金属结核分布之间的映射关系,探讨地形坡度对多金属结核分布的控制作用.结果表明,在经度、纬度、水深、坡度四个因素中,坡度对结核分布影响程度最大;多金属结核主要分布于坡度小于5°的地方;当坡度小于5°时,丰度与地形坡度呈正相关,品位与地形坡度呈负相关,丰度与品位呈负相关;品位与坡度似呈指数关系,坡度增大,品位降低.     

Kohonen网络与BP网络的集成应用研究

本文介绍了 Kohonen神经网络对输入数据进行聚类方法在卷烟配方中的应用 ,提出了从核心样本动态搜索 BP网络训练样本的新探索 ,摒弃了过去 BP算法中训练样本固定不变 ,互不相交的方法 ,实现了 BP网络和 Kohonen网络动态无缝集成。     

水下智能潜器的神经网络运动控制

本文介绍一种基于神经网络的水下智能潜器的运动控制方法，该方法通过在线学习，融控制与滤波为一体。计算机仿真与水池实验验证表明，该方法的控制与滤波性能良好，对环境的学习与适应能力强。该方法事实上可用于一般动力系统的控制。     

一种基于BP算法学习的小波神经网络

为发展 Szu的基于信号表示的小波神经网络 ,提出一种多输入多输出的小波网络模型 ,网络隐层采用框架小波函数、输出层采用 Sigmoid激励函数 ,并选用“熵误差函数”以加速网络的学习速度。奇偶判别和混沌时间序列预测例子的实验结果表明了它具有良好的函数逼近能力和推广能力 ,收敛速度和均方误差均优于相同结构的多层感知器模型。     

人工神经网络在地质学判别分析中的应用

将现代人工神经网络方法应用于地质学的判别分析，通过实例计算表明，无论是对于两组判别分析还是多组判别分析，人工神经网络方法都是切实可行的。     

Prediction of Load Carrying Capacity of Corroded Reinforced Concrete Beam

A novel method for prediction of the load carrying capacity of a corroded reinforced concrete beam (CRCB) is presented in the paper. Nine reinforced concrete beams, which had been working in an aggressive environment for more than 10 years, were tested in the laboratory. Comprehensive tests, including flexural test, strength test for corroded concrete and rusty rebar, and pullout test for bond strength between concrete and rebar, were condueted. The flexural test results of CRCBs reveal that the distribution of surface cracks on the beams shows a fractal behavior. The relationship between the fractal dimensions and mechanical properties of CRCBs is then studied. A prediction model based on artificial neural network (ANN) is established by the use of the fractal dimension as the corrosion index, together with the basic intbrmation of the beam. The validity of the prediction model is demonstrated through the experimental data, and satisfactory resuits are achieved.     

沉降监测多项式回归分析与神经网络预测

使用多项式和切比雪夫(Tchebyshev)多项式分别对沉降监测数据进行回归分析以预测未来沉降值,其中切比雪夫多项式的外推效果较好;应用前向BP神经网络对两种不同的单因子输入模式进行非线性函数逼近,并进行了不同采样步长的比较,实例表明将时间点作为网络的输入对沉降进行预测效果较好。