Development of an improved three-dimensional rough discrete fracture network model:Method and application

Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.

基于Rough Set和neural network组合数据挖掘

提出了一种基于rough set和neural network的数据挖掘新方法。首先利用粗集理论对原始数据进行一致性属性约简,然后使用神经网络对数据进行学习,并同时完成属性的不一致约简,最后再由粗集对神经网络中的知识进行规则抽取。该方法充分融合了粗集理论强大的属性约简、规则生成能力和神经网络优良的分类、容错能力。实验表明,该方法快速有效,生成规则简单准确,具有良好的鲁棒性。

Intelligent Intrusion Detection System Model Using Rough Neural Network

A model of intelligent intrusion detection based on rough neural network (RNN), which combines the neural network and rough set, is presented. It works by capturing network packets to identify network intrusions or malicious attacks using RNN with sub-nets. The sub-net is constructed by detection-oriented signatures extracted using rough set theory to detect different intrusions. It is proved that RNN detection method has the merits of adaptive, high universality, high convergence speed, easy upgrading and management.

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 harmful vibration of the offshore jacket platform.

Neural Network Based on Rough Sets and Its Application to Remote Sensing Image Classification

This paper presents a new kind of back propagation neural network (BPNN) based on rough sets,called rough back propagation neural network (RBPNN).The architecture and training method of RBPNN are presented and the survey and analysis of RBPNN for the classification of remote sensing multi_spectral image is discussed.The successful application of RBPNN to a land cover classification illustrates the simple computation and high accuracy of the new neural network and the flexibility and practicality of this new approach.

Rough Set Based Fuzzy Neural Network for Pattern Classification

A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.

Yarn Quality Prediction and Diagnosis Based on Rough Set and Knowledge-Based Artificial Neural Network

In the spinning process, some key process parameters( i. e.,raw material index inputs) have very strong relationship with the quality of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a hybrid learning-based model was developed for on-line intelligent monitoring and diagnosis of the spinning process. In the proposed model, a knowledge-based artificial neural network( KBANN) was developed for monitoring the spinning process and recognizing faulty quality categories of yarn. In addition,a rough set( RS)-based rule extraction approach named RSRule was developed to discover the causal relationship between textile parameters and yarn quality. These extracted rules were applied in diagnosis of the spinning process, provided guidelines on improving yarn quality,and were used to construct KBANN. Experiments show that the proposed model significantly improve the learning efficiency, and its prediction precision is improved by about 5. 4% compared with the BP neural network model.

Neural Network Modeling and Prediction of Surface Roughness in Machining Aluminum Alloys

Artificial neural network is a powerful technique of computational intelligence and has been applied in a variety of fields such as engineering and computer science. This paper deals with the neural network modeling and prediction of surface roughness in machining aluminum alloys using data collected from both force and vibration sensors. Two neural network models, including a Multi-Layer Perceptron (MLP) model and a Radial Basis Function (RBF) model, were developed in the present study. Each model includes eight inputs and five outputs. The eight inputs include the cutting speed, the ratio of the feed rate to the tool-edge radius, cutting forces in three directions, and cutting vibrations in three directions. The five outputs are five surface roughness parameters. Described in detail is how training and test data were generated from real-world machining experiments that covered a wide range of cutting conditions. The results show that the MLP model provides significantly higher accuracy of prediction for surface roughness than does the RBF model.

Two Hybrid Methods Based on Rough Set Theory for Network Intrusion Detection

In this paper,we propose two intrusion detection methods which combine rough set theory and Fuzzy C-Means for network intrusion detection.The first step consists of feature selection which is based on rough set theory.The next phase is clustering by using Fuzzy C-Means.Rough set theory is an efficient tool for further reducing redundancy.Fuzzy C-Means allows the objects to belong to several clusters simultaneously,with different degrees of membership.To evaluate the performance of the introduced approaches,we apply them to the international Knowledge Discovery and Data mining intrusion detection dataset.In the experimentations,we compare the performance of two rough set theory based hybrid methods for network intrusion detection.Experimental results illustrate that our algorithms are accurate models for handling complex attack patterns in large network.And these two methods can increase the efficiency and reduce the dataset by looking for overlapping categories.

Rough set and radial basis function neural network based insulation data mining fault diagnosis for power transformer

Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose “confidence” and “support” is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose “confidence and support” is lower than requirement, are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e., as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

Use of Rough Sets Theory in Point Cluster and River Network Selection

In this paper, we applied the rough sets to the point cluster and river network selection. In order to meet the requirements of rough sets, first, we structuralize and quantify the spatial information of objects by convex hull, triangulated irregular network (TIN), Voronoi diagram, etc.;second, we manually assign decisional attributes to the information table according to conditional attributes. In doing so, the spatial information and attribute information are integrated together to evaluate the importance of points and rivers by rough sets theory. Finally, we select the point cluster and the river network in a progressive manner. The experimental results show that our method is valid and effective. In comparison with previous work, our method has the advantage to adaptively consider the spatial and attribute information at the same time without any a priori knowledge.

A theoretical and deep learning hybrid model for predicting surface roughness of diamond-turned polycrystalline materials

Polycrystalline materials are extensively employed in industry.Its surface roughness significantly affects the working performance.Material defects,particularly grain boundaries,have a great impact on the achieved surface roughness of polycrystalline materials.However,it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods.In this work,a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed.The kinematic–dynamic roughness component in relation to the tool profile duplication effect,work material plastic side flow,relative vibration between the diamond tool and workpiece,etc,is theoretically calculated.The material-defect roughness component is modeled with a cascade forward neural network.In the neural network,the ratio of maximum undeformed chip thickness to cutting edge radius RT S,work material properties(misorientation angle θ_(g) and grain size d_(g)),and spindle rotation speed n s are configured as input variables.The material-defect roughness component is set as the output variable.To validate the developed model,polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools.Compared with the previously developed model,obvious improvement in the prediction accuracy is observed with this hybrid prediction model.Based on this model,the influences of different factors on the surface roughness of polycrystalline materials are discussed.The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed.Two fracture modes,including transcrystalline and intercrystalline fractures at different RTS values,are observed.Meanwhile,optimal processing parameters are obtained with a simulated annealing algorithm.Cutting experiments are performed with the optimal parameters,and a flat surface finish with Sa 1.314 nm is finally achieved.The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.

基于邻域粗集神经网络的大数据特征分类系统

为提升主机元件对大数据的分类准确性,尽可能地避免数据误传,提出基于邻域粗集神经网络的大数据特征分类系统。在邻域粗集神经网络中,完成对邻域系数的粒化处理,通过逼近运算的方式,使神经网络模型快速趋于稳定。选取大数据特征调制信息,借助调制识别器元件控制大数据特征的导出方向,结合关联信道组织完成数据特征的多标合并处理。实验表明,利用该系统可将大数据的单位召回率提升至65%,能够促进主机元件对大数据的准确分类。

医用氧化锆陶瓷磨削表面粗糙度的声发射智能预测

医用氧化锆陶瓷(Y-TZP)是较好的齿科修复体材料,为了得到较好的齿科修复体性能对于其制造精度特别是表面粗糙度的要求比较高,但其是硬脆难加工材料,为了提高医用氧化锆陶瓷磨削加工表面质量和加工效率,在对医用氧化锆陶瓷磨削过程中的声发射信号分频段进行相关性分析的基础上,提取磨削声发射840~850kHz敏感频段信号中与磨削表面粗糙度强相关的12组特征值,构建了具有较高预测精度的随机森林神经网络,最终医用氧化锆陶瓷磨削表面粗糙度声发射预测最大相对误差低于8.37%,研究结果对医用氧化锆陶瓷磨削表面粗糙度在线智能监测有较大的参考价值。

基于机器学习耦合模型预测FDM零件的表面粗糙度

熔融沉积工艺(FDM)制造的零件表面粗糙度高,不仅影响了零件外观,还降低了性能。采用响应面实验设计,研究了层高(A)、填充密度(B)、喷嘴温度(C)、床层温度(D)和打印速度(E)对聚乳酸(PLA)零件表面粗糙度的影响。同时,将遗传算法(GA)与决策树(DT)、人工神经元网络(ANN)两种机器学习模型相结合,预测了零件的表面粗糙度。结果表明,A、B、C和E是显著影响零件表面粗糙度的主效应,A×B、A×C、A×E、B×C、B×E、C×E是影响显著的交互效应。GA+DT耦合模型预测PLA零件表面粗糙度的准确性更高,预测值与实验值的相关系数(R2)、均方误差(MSE)和平均绝对误差(MAE)分别为0.952、0.132和0.234,优于GA+ANN的0.823、1.561和1.759。GA+DT模型的预测值与实验值的Pearson相关系数为0.984,而GA+ANN模型仅为0.903,这表明GA+DT模型在预测PLA零件表面粗糙度时准确度更高。

地理信息知识获取Rough-NN模型研究

提出了一种粗糙集结合神经网络的粗糙集神经网络模型,对具有高度自相关性的地理信息进行知识获取.主要思想是利用辨别矩阵形成约简算法,得到最简的if-then规则;然后构造三层神经网络模拟最简规则,其中网络的输入输出由本文提出的参数训练方法确定.本文利用VB实现该模型,并对松花江流域的洪涝干旱灾情进行了仿真实验,结果表明该模型可以快速地获取最简的if then规则,得到正确的决策结果.*

一种基于Rough Sets和模糊神经网络的规则获取的方法

该文提出了一种基于RoughSets思想获取初始规则，并通过模糊神经网络优化，最后再进行简化获取模糊规则，及模糊系统参数学习的方法。并通过实例进行了自动列车运行系统仿真。文中还基于上述实例，将这种基于模糊神经网络的学习与控制方法与标准的BP网络和基本的模糊系统方法进行了比较，并总结了这种方法的特点。结论表明，该文所提出的模糊规则生成和模糊系统学习方法是行之有效的。

基于Rough集和神经网络的烧结过程异常诊断研究

为了及时、准确诊断烧结过程的异常状况并及时消除异常,本文将Rough集和神经网络相结合,建立了烧结过程异常状况智能诊断系统。基本思想是首先利用Rough集对知识库进行约简,然后利用神经网络对约简后的知识进行分层融合。该系统具有简化样本、适应性强和不易陷入局部最小点等特点,能有效处理异常中的噪声或不相容的信息。