基于ABAQUS-ANFIS-MCS的岩质边坡可靠性分析

针对岩质边坡工程稳定性分析中参数的不确定性,基于ABAQUA建立了平面破坏型边坡有限元分析模型。并用该模型进行了边坡稳定状态的数值模拟,以获得进行ANFIS分析的数据。同时基于自适应神经模糊推理系统建立了岩体力学参数与边坡抗滑力和下滑力的映射模型,分析得到抗滑力和下滑力的统计特征。根据蒙特卡罗模拟方法用MATLAB语言编写了求解边坡的破坏概率和可靠度的计算程序,对湖南雪峰水泥原料矿山的露天矿边坡进行可靠度分析。研究结果表明,该方法具有避免编写冗长的有限元计算程序、节省机时、计算精度高的优点。     

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。     

一种基于小波分解和模糊推理神经网络的潮位预测方法

潮位预测严重影响沿海区域,尤其是近海浅水沿岸地区居民的生产生活和涉海活动。谐波分析是长周期潮位预测的传统方法,但无法预测非周期性气象过程发生时的水位变化。与数据处理方法相结合,人工智能的方法通过拟合输入与输出数据的历史数值关系,能够有效预测高度非线性和非平稳的流模式,因而在时间序列数据预测领域得到了广泛的应用。本文结合自适应模糊推理系统（Adaptive Neuro-Fuzzy Inference System, ANFIS）和小波分解方法,利用水位异常和风切变分量作为输入数据,实现了一种综合的多时效潮位预测方法。文中测试了多种输入变量组合和小波-ANFIS（WANFIS）模型,并与人工神经网络（Artificial Neural Network, ANN）、小波-ANN（WANN）和ANFIS模型进行了预测结果对比。通过不同指数的误差分析来看,相比ANN模型,ANFIS模型能够更准确的预测潮位变化,小波分解对ANFIS预测精度有一定的提高,且模型中水位异常和风切变分量数据的加入比单一的潮位数据输入能取得更好的预测结果。     

大坝变形预测的ANFIS模型

大坝变形预报时,存在影响因素多且各因素之间的相互关系复杂,常规的变形预测方法难以满足大坝安全监控的要求。自适应神经模糊系统(ANFIS)兼备神经网络的自学习、自适应能力,以及模糊系统良好的知识表达性能。在系统分析大坝变形主要影响因素的基础上,以水库库水位、温度及时间效应为影响因子,建立基于自适应模糊神经系统的大坝变形预测模型,并以三峡二期围堰为例进行实证分析。研究表明,该模型计算简便,适用性强,精度高,为大坝变形预报提供了新的思路。     

A modelling approach for estimating suspended sediment concentrations for multiple rivers influenced by agriculture

A data-driven model based on an adaptive neuro-fuzzy inference system (ANFIS) was tested for the estimation of suspended sediment concentrations within watersheds influenced by agriculture. ANFIS models were developed using different combinations of inputs such as precipitation, streamflow, surface runoff and the watershed vulnerability index. A multi-watershed ANFIS model was also developed combining the datasets from all studied watersheds. The best results were obtained from a combination of precipitation, streamflow and watershed vulnerability index as input variables. Nash-Sutcliffe coefficients were improved for the multi-watershed ANFIS compared to watershed-specific ANFIS models. The introduction of the erosion vulnerability index significantly improved the ability of the ANFIS model to estimate suspended sediment concentrations within the watersheds. Furthermore, the inclusion of this index opens the possibility of using the ANFIS model to investigate the impact of land-use changes on sediment delivery.     

基于ANFIS的香梨始花期预测研究

应用自适应模糊神经推理系统(Adaptive network-based fuzzy inference system, ANFIS)对库尔勒地区香梨始花期进行了预测,3月平均气温、3月下旬平均气温、5厘米地温、10厘米地温、2月下旬日照、3月上旬日照、3月中旬日 照、3月下旬日照8个指标为输入变量,香梨始花期单项指标作为输出,结果表明：预测的平均相对误差为2.51%,经t检验和回归分析表明预测值和实测值相差不大,相关系数为0.9286,具有较好的一致性,模型具有较高的精度和稳定性,说明ANFIS模型可以很好的描述香梨始花期和气象因子的关系,为香梨始花期的进一步研究提供了参考。     

Prediction of coarse particle movement with adaptive neuro‐fuzzy inference systems

The use of a neuro‐fuzzy approach is proposed to model the dynamics of entrainment of a coarse particle by rolling. It is hypothesized that near‐bed turbulent flow structures of different magnitude and duration or frequency and energy content are responsible for the particle displacement. A number of Adaptive Neuro‐Fuzzy Inference System (ANFIS) architectures are proposed and developed to link the hydrodynamic forcing exerted on a solid particle to its response, and model the underlying nonlinear dynamics of the system. ANFIS combines the advantages of fuzzy inference (If‐Then) rules with the power of learning and adaptation of the neural networks. The model components and forecasting procedure are discussed in detail. To demonstrate the model's applicability for near‐threshold flow conditions an example is provided, where flow velocity and particle displacement data from flume experiments are used as input and output for the training and testing of the ANFIS models. In particular, a Laser Doppler velocimeter (LDV) is employed to obtain long records of local streamwise velocity components upstream of a mobile exposed particle. These measurements are acquired synchronously with the time history of the particle's position detected by a setup including a He‐Ne laser and a photodetector. The representation of the input signal in the time and frequency domain is implemented and the best performing models are found capable of reproducing the complex dynamics of particle response. Following a trial and error approach the different models are compared in terms of their efficiency and forecast accuracy using a number of performance indices. Copyright © 2011 John Wiley & Sons, Ltd.     

基于相空间重构与模糊神经网络的海温垂直分布预测模型

在海温预报中引入混沌理论,将相空间重构与模糊神经网络相结合,提出了海温垂直建模预测模型。通过相空间重构,把海温时间序列拓展为多维序列,而多维序列包含着各态历经的信息,从而挖掘出了丰富的海温变化空间的信息,有利于模糊神经网络的训练。利用建立好的模糊神经网络模型,较好地对海温的垂直结构进行了建模、训练和预测。实际的预测结果表明,该模型预报精度较高,超前1~5个月的预测值的相对误差均控制在10%以内,预测结果可以为业务工作提供一定的参考与借鉴。     

Longshore sediment transport estimation using a fuzzy inference system

Accurate prediction of longshore sediment transport in the nearshore zone is essential for control of shoreline erosion and beach evolution. In this paper, a hybrid Adaptive-Network-Based Fuzzy Inference System (ANFIS), Fuzzy Inference System (FIS), CERC, Walton–Bruno (WB) and Van Rijn (VR) formulae are used to predict and model longshore sediment transport in the surf zone. The architecture of ANFIS consisted of three inputs (breaking wave height), (breaking angle), (wave period) and one output (longshore sediment transport rate). For statistical comparison of predicted and measured sediment transport, bias, root mean square error and scatter index are used. The longshore sediment transport rate (LSTR) and wave characteristics at a 4 km-long beach on the central west coast of India are used as case studies. The CERC, WB and VR methods are also applied to the same data. Results indicate that the errors of the ANFIS model in predicting wave parameters are less than those of the empirical formulas. The scatter index of the CERC, WB and VR methods in predicting LSTR is 51.9%, 27.9% and 22.5%, respectively, while the scatter index of the ANFIS model in the prediction of LSTR is 17.32%. A comparison of results reveals that the ANFIS model provides higher accuracy and reliability for LSTR estimation than the other techniques.     

基于混合模糊神经网络储层裂缝地震反演研究

基于储层裂缝系统具有非线性特征,储层裂缝地震反演是由遗传算法(GA)、模糊神经网络(ANF IS)和禁忌搜索算法(TS)有机地结合而构成的自适应混合模糊神经网络技术。该技术在成像测井约束下,形成的自适应混合算法分别训练ANF IS网络的前提参数和结论参数,从而获得满足精度要求的储层裂缝密度的最佳估计值。针对目标储层段,应用储层裂缝地震反演方法对过井地震剖面和联井地震剖面进行了储层裂缝密度反演处理,获得了可用于地质解释和油气预测的视裂缝密度剖面。这种裂缝密度剖面含有裂缝定量信息,其裂缝密度相对误差为:0.8%~24%,满足勘探开发的要求。经与研究区的地质对比分析表明,视裂缝密度剖面上的裂缝展布特征符合研究区的沉积相分布和岩石力学性质的变化特征,对研究区的勘探开发具有重要意义。