堤坝检测水下机器人模糊自适应控制方法研究

GDROV是用于堤坝探测的水下机器人,设计上属于开架式机器人,其精确的数学模型很难获得.采用基于模糊逻辑的直接自适应控制方法,利用模糊基函数网络逼近理想控制输出,通过模糊逻辑动态调整控制器的参数自适应律,可有效解决水下机器人控制问题.建立GDROV的水动力模型,给出基于模糊逻辑的直接自适应控制算法,最后通过仿真试验和外场试验验证了该控制器对模型的不确定性具有较强的鲁棒性,且具有良好的跟踪性能.     

基于遗传算法的超声回弹综合法混凝土测强公式

利用30个混凝土试块的超声波声速、回弹、抗压强度实测数据,应用遗传算法建立了新的超声回弹的混凝土强度换算值计算公式,该公式明显改进了高强度区混凝土强度的拟合精度。结果表明,作为一种全局搜索技术,遗传算法在超声回弹综合法测强曲线研制过程中具有实际应用价值。     

超声回弹综合法检测单一构件混凝土强度推定值的保证率分析

在实际工程检测数据的基础上,利用M on te C arlo试验对单一构件混凝土强度推定值的保证率问题进行了分析。30片梁板实测数据分析结果表明:将构件各测区混凝土强度换算值的最小值作为该构件的混凝土强度推定值的保证率范围为79.0%~94.2%,小于《超声回弹综合法检测混凝土强度技术规程》(CECS 02:88)规定的95%保证率要求,因此,对结构性能鉴定而言该混凝土强度推定值是偏于不安全的,应引起试验检测人员的充分重视。     

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.     

基于神经-模糊方法的单料烟感官质量评价专家系统

作者通过对单料烟评吸的结果与理化测定的指标参数进行分析 ,结合专家经验并采用神经 -模糊方法 ,提出一种基于单料烟的理化指标对各感官参数进行分类、分级 ,建造单料烟感官质量评价专家系统的方法。实验表明 ,该系统具有学习与知识提取能力 ,在卷烟产品质量管理新产品开发中具有指导意义     

基于模糊神经网络的多变量系统的解耦方法

针对多变量、强耦合、纯迟延系统,提出一种模糊神经网络的解耦方法,结合遗传算法、将多变量系统解耦成单变量系统。传统解耦方法对于非线性系统、变结构系统以及耦合关系和耦合强度随时间和负载变化的复杂系统经常无能为力,而这种综合了模糊逻辑和神经网络优势的解耦方法,由于具有非线性和自学习能力,使其解耦性能不受影响,弥补了传统解耦方法的缺陷,对复杂系统有着较好的解耦能力。且该方法不需要建立精确的数学模型,易于实现。文章最后通过仿真实验验证了该模型的解耦效果。     

基于多态模糊贝叶斯网络的半潜平台安装风险分析

针对半潜式平台安装作业风险评估中存在的风险多态性和模糊性问题,构建了一种多态模糊贝叶斯网络风险分析模型。根据行业规范推荐标准定义语言性评价模糊集,描述根节点的事故状态发生概率,克服了传统方法中确定性概率难以获取的困难。利用相似性聚合法结合置信度指标融合专家意见,引入改进的去模糊化转换方法,提高了专家知识经验转化为定量数据的合理性和可靠性。基于贝叶斯网络的双向推理和敏感性分析技术,实现了工程作业全过程的风险评估。通过对陵水17-2项目半潜平台整体吊装过程进行风险分析,验证该模型的合理性与有效性,为半潜平台安装作业风险管理与防控策略制定提供指导。     

基于模糊集理论的海塘稳定可靠度研究

针对海塘边坡稳定性分析中因忽略参数空间变化等因素产生的分析结果偏于不安全的倾向,提出了一种计算模糊随机可靠度的简化方法.首次引入"正态模糊数"来描述所选重要参数的计算区间,采用简化Bishop法与"模糊顶点法"相结合的方法计算边坡稳定的安全系数密度函数,然后根据大量边坡实例统计结果的构造函数得到安全系数的戒下型隶属函数,最终计算获得边坡稳定的模糊随机可靠度评价.结果分析表明,传统可靠度分析结果偏于不安全,而利用模糊随机可靠度来评价边坡稳定状况更趋于合理性.     

基于自适应卡尔曼滤波的多波束数据改正方法

多波束测深数据质量受声速误差等因素影响较大,针对该情况,利用自适应卡尔曼滤波以相邻条带中央波束作为先验信息,对多波束测深数据进行改正。首先,以相邻条带的中央波束数据构建海底地形大致走向的趋势线作为先验信息,结合观测值与检查线测深值,得到观测值和先验信息的偏差;其次,利用卡尔曼滤波对观测值进行改正并对方法进行分析;最后,利用自适应卡尔曼滤波对多波束测深数据进行优化。实验表明:利用自适应卡尔曼滤波能够对声速整体误差影响的多波束测深数据进行有效改正。     

El Nio/La Nia过程影响因子的模糊诊断分析

基于模糊系统理论，讨论了从实测信号中滤除特定干扰噪音的途径和过程，研究了从观测资料中辩识El Nino／La Nina主要影响因子的诊断检测方法。结果表明，由于模糊系统具有非线性、容错性和自适应学习等特性，因此能够比较有效地辨认和检测出El Nino／La Nina事件的主要影响因子，并大致分析出它们对不同El Nino／La Nina事件的影响程度和贡献大小。     

基于自适应神经模糊推理系统的GPS高程转换

在工程实践中需要将GPS高转换为正常高。近年来,神经网络方法已经用到这一目的,并且取得一定成果。然而这种方法有一些缺点。一种新方法ANFIS被用来解决这一问题。首先简单介绍ANFIS理论,然后介绍使用ANFIS转换GPS高程的具体过程,最后给出一个使用本方法的工程实例及相应的结果。     

Estimation of pile group scour using adaptive neuro-fuzzy approach

An accurate estimation of scour depth around piles is important for coastal and ocean engineers involved in the design of marine structures. Owing to the complexity of the problem, most conventional approaches are often unable to provide sufficiently accurate results. In this paper, an alternative attempt is made herein to develop adaptive neuro-fuzzy inference system (ANFIS) models for predicting scour depth as well as scour width for a group of piles supporting a pier. The ANFIS model provides the system identification and interpretability of the fuzzy models and the learning capability of neural networks in a single system. Two combinations of input data were used in the analyses to predict scour depth: the first input combination involves dimensional parameters such as wave height, wave period, and water depth, while the second combination contains nondimensional numbers including the Reynolds number, the Keulegan–Carpenter number, the Shields parameter and the sediment number. The test results show that ANFIS performs better than the existing empirical formulae. The ANFIS predicts scour depth better when it is trained with the original (dimensional) rather than the nondimensional data. The depth of scour was predicted more accurately than its width. A sensitivity analysis showed that scour depth is governed mainly by the Keulegan–Carpenter number, and wave height has a greater influence on scour depth than the other independent parameters.     

基于调和分析法与ANFIS系统的综合潮汐预报模型

港口沿岸地区以及河流入海口等地区的精确潮汐预报对于各种海洋工程作业有着非常重要的意义。潮汐水位的变化受到众多复杂因素的影响,而且这些复杂的因素往往有着较强的实变性和非线性。为了进一步提高沿岸港口码头等水域的潮汐水位的预测精度,本文提出了一种基于调和分析模型与自适应神经模糊推理系统相结合的模块化潮汐水位预测模型;并采用相关分析确定整个预测模型的输入维数;模块化将潮汐分解为两部分：由天体引潮力形成的天文潮部分和由各种天气以及环境因素引起非天文潮部分。其中调和分析法用于天文潮部分的预测,ANFIS用于预测具有较强非线性的非文潮部分。模块化综合了两种方法的优势,即调和分析法能够实现长期、稳定的天文潮预报,ANFIS能够以较高的精度实现潮汐非线性拟合与预测。模型使用ANFIS模型和调和分析模型分别对潮汐的非天文潮和天文潮部分进行仿真预测,然后将两部分的预测结果综合形成最终的潮汐预测值。此外,本文选用三种不同的模糊规则生成方法（grid partition （GP）,fuzzy c-means （FCM） and sub-clustering （SC））生成完整的ANFIS系统,并使用实测数据进行验证用以选取最优的ANFIS预测模型。最后将最优的ANFIS模型与调和分析模型相结合进行潮汐水位的最终预报。仿真实验选用Fort Pulaski潮汐观测站的实测潮汐值数据进行预报的仿真实验,仿真结果验证了该模型的可行性与有效性并取得了良好的效果,具有较高的预报精度。     

Linear genetic programming to scour below submerged pipeline

Genetic programming (GP) has nowadays attracted the attention of researchers in the prediction of hydraulic data. This study presents Linear Genetic Programming (LGP), which is an extension to GP, as an alternative tool in the prediction of scour depth below a pipeline. The data sets of laboratory measurements were collected from published literature and were used to develop LGP models. The proposed LGP models were compared with adaptive neuro-fuzzy inference system (ANFIS) model results. The predictions of LGP were observed to be in good agreement with measured data, and quite better than ANFIS and regression-based equation of scour depth at submerged pipeline.     

Application of a fuzzy inference system for the prediction of longshore sediment transport

A fuzzy inference system (FIS) and a hybrid adaptive network-based fuzzy inference system (ANFIS), which combines a fuzzy inference system and a neural network, are used to predict and model longshore sediment transport (LST). The measurement data (field and experimental data) obtained from Kamphuis [1] and Smith et al. [2] were used to develop the model. The FIS and ANFIS models employ five inputs (breaking wave height, breaking wave angle, slope at the breaking point, peak wave period and median grain size) and one output (longshore sediment transport rate). The criteria used to measure the performances of the models include the bias, the root mean square error, the scatter index and the coefficients of determination and correlation. The results indicate that the ANFIS model is superior to the FIS model for predicting LST rates. To verify the ANFIS model, the model was applied to the Karaburun coastal region, which is located along the southwestern coast of the Black Sea. The LST rates obtained from the ANFIS model were compared with the field measurements, the CERC [3] formula, the Kamphuis [1] formula and the numerical model (LITPACK). The percentages of error between the measured rates and the calculated LST rates based on the ANFIS method, the CERC formula (K_sig = 0.39), the calibrated CERC formula (K_sig = 0.08), the Kamphuis [1] formula and the numerical model (LITPACK) are 6.5%, 413.9%, 6.9%, 15.3% and 18.1%, respectively. The comparison of the results suggests that the ANFIS model is superior to the FIS model for predicting LST rates and performs significantly better than the tested empirical formulas and the numerical model.     

Calibration of a two-dimensional hydrodynamics model

This paper reports the approach and results of calibrating a two-dimensional hydrodynamics model. The model was applied to Humboldt Bay, California, and calibrated with synoptic tidal data at four locations. The model calibration was done by using both a trial-and-error approach and a parameter identification (PI) method. For the given finite-difference grid resolution and field observations, the calibration attempt revealed that the two methods produced two different sets of parameters, but with almost identical comparisons between the model solutions and observations. The study results indicate that the appropriate range of model parameter values can be more efficiently identified by parameter identification method, and the best calibration strategy is to use both methods conjunctively.     

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide(a.k.a algal bloom) model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.     

基于BP人工神经网络的人工海水侵蚀对混凝土强度影响的试验研究

混凝土受盐害侵蚀破坏直接影响混凝土的强度和耐久性。针对混凝土受盐害侵蚀破坏功能函数不能明确表达及非线性程度高的特点,利用BP人工神经网络进行分析,在大量试验数据基础上,通过计算方法的优化和样本的训练,对隐含层和各隐含单元多次试取,最优选取trainglm训练函数,建立盐害预测的人工神经网络系统。解析结果表明,混凝土试件抗压强度预测值和试验实测值的相对误差较小,建立的人工神经网络模型具有较高的预测精度。     

A Bayesian method for the estimation of return values of wave heights

Bayesian statistics offer a novel means of estimating return values of wave heights and hence of establishing design criteria for offshore structures. The Bayesian method has significant advantages over the classical method since it enables all types of uncertainty (physical, parameter, distribution) associated with the design wave prediction to be handled in a consistent manner in the same analysis.The basic principles of the Bayesian method for drawing inferences are outlined step-by-step. It is shown how Bayesian estimators of return values for wave heights are established by taking an expectation over all parameters and contending distributions. When the Bayesian procedure is applied to large data sets, such as wave data sets, computational difficulties could be encountered, making a “remedial” procedure necessary. However, the Bayesian procedure has been used successfully with wave data sets from the northern North Sea. Furthermore, the associated remedial procedure is such that the program can be made suitable for many existing computers, e.g. desk computers.