网络数学模型与人工智能数学公式表示法简述

通过对网络结构的分析,可以将网络模型用数学公式来表示,主要解决怎么把复杂的网络转化为简单逻辑数学的问题。通过运算分析对网络进行的优化,可以进而抽象出一个更一般的网络数学模型,亦可生成多种新的数学模型,使得网络结构关系更简单实用、安全可信,并且更适用于多种类型网络数学模型的应用环境。除此之外,将网络数学公式应用于人工智能网络上,可使人工智能网络更简单化,便于人工智能网络的分析计算。

基于贝叶斯网络数学模型的无线网络安全风险检测方法

无线网络安全风险检测方法使用通用漏洞评分系统(Common Vulnerability Scoring System,CVSS)标度,确定网络潜在漏洞风险源,易受漏洞逻辑关系限制,导致网络安全风险检测性能较差。因此,提出基于贝叶斯网络数学模型的无线网络安全风险检测方法。以贝叶斯分类器为核心,构建网络安全风险检测贝叶斯网络数学模型,设计无线网络安全风险检测算法,实现无线网络安全风险检测。实验结果表明,利用该方法可以检测不同的安全风险漏洞。

66kV及以下电压等级系统故障分析的网络数学模型研究

本文根据66kV 及以下等级电力系统网络的特点,应用拓扑理论对其网络数学模型进行了有效的简化,大大地节省了计算机内存。特别是在故障分析计算中,当运行方式多变、需反复修改网络模型时,本文的方法可使计算速度明显加快。现已在沈阳地区电网继电保护计算机整定计算中应用,为推广使用微型计算机进行较大规模电网的故障分析计算创造了条件。

基于数学模型的5G通信网络资源分配优化研究

文章研究了5G通信网络中的资源分配优化问题,通过构建线性规划数学模型,并采用单纯形法进行求解,应用结果表明,优化后的资源分配方案显著提高了网络的整体性能,包括网络吞吐量和用户满意度,并对不同资源分配策略进行了对比分析,验证了数学模型在5G通信网络性能优化中的有效性。

基于DSP的永磁同步电机神经网络逆解耦控制

针对永磁同步电机(PMSM)这一非线性多变量的复杂系统,提出了不依赖对象精确数学模型和参数的PMSM神经网络逆系统控制方法,并在以数字信号处理器(DSP)为核心的控制实验平台上得以验证。PMSM的逆系统由静态神经网络加积分器构成并与原系统串联,实现了其转速和磁链的动态解耦。在此基础上,为两个解耦的伪线性子系统设计了线性闭环调节器,使整个系统获得优良的动静态性能。实验结果表明,神经网络逆系统方法可以实现对PMSM的高性能控制,对参数变化和负载扰动具有较强的鲁棒性。

人数少于任务数的全指派问题的迭代算法

针对人数少于任务数的情况,按每人至少承担一项任务,至多承担L项任务,但每项任务只允许一人承担的指派原则,给出了一种求解这种指派问题的迭代算法,该算法操作简便、易于用计算机运行。构建该算法的方法,用于某些其它指派问题,可以使相应的算法更加便捷。

Progress in Pressure Swing Adsorption Models During the Recent 30 Years

The pressure swing adsorption (PSA) models discussed here are divided into three categories: partialdifferential equation model, electrical analogue model and neural network model. The partial differential equationmodel, including equilibrium and kinetic models, has provided an elementary viewpoint for PSA processes. Usingthe simplest equilibrium models, some influential factors, such as pressurization with product, incomplete purge,beds with dead volume and heat effects, are discussed respectively. With several approximate assumptions i.e.,concentration profile in adsorbent, 'frozen' column, symmetry and heat effects of bed wall, the more complexkinetic models can be simplified to a certain degree at the expense of a limited application. It has also been foundthat the electrical analogue model has great flexibility to handle more realistic PSA processes without any additionalhypothesis.

Application of neural network model coupling with the partial least-squares method for forecasting watre yield of mine

Scientific forecasting water yield of mine is of great significance to the safety production of mine and the colligated using of water resources. The paper established the forecasting model for water yield of mine, combining neural network with the partial least square method. Dealt with independent variables by the partial least square method, it can not only solve the relationship between independent variables but also reduce the input dimensions in neural network model, and then use the neural network which can solve the non-linear problem better. The result of an example shows that the prediction has higher precision in forecasting and fitting.

Orthogonal moment based texture segmentation

Texture segmentation is a necessary step to identify the surface or an object in an image. We present a Legendre moment based segmentation algorithm. The Legendre moments in small local windows of the image are computed first and a nonlinear transducer is used to map the moments to texture features and these features are used to construct feature vectors used as input data. Then an RBF neural network is used to perform segmentation. A k-mean algorithm is used to train the hidden layers of the RBF neural network. The training of the output layer is the supervised algorithm based on LMS. The algorithm has been successfully used to segment a number of gray level texture images. Compared with the geometric moment-based texture segmentation, we can reduce the error rates using orthogonal moments.

山区高速公路桥梁预防性养护技术措施研究

针对山区高速公路桥梁大纵坡、平曲线弯桥、纵横跨度大的特点,结合高速公路及桥梁工程设计原则、施工、运营和养护情况,运用公路养护巡检系统与桥梁健康监测系统,建立了山区高速公路桥梁预防性养护技术理念。基于耐久性全寿命设计原则提出山区高速公路桥梁预防性养护技术措施,结果表明:预防性养护技术措施可全方位掌控高速公路以及桥梁工程运营情况,有效提高养护管理水平,在保证山区高速公路桥梁结构全寿命安全、可靠运营中具有积极作用。

Analysis of distribution uniformity of nodes in wireless sensor networks

Wireless sensor networks have several special characteristics which make against the network coverage, such as shortage of energy, difficulty with energy supply and so on. In order to prolong the lifetime of wireless sensor networks, it is necessary to balance the whole network load. As the energy consumption is related to the situation of nodes, the distribution uniformity must be considered. In this paper, a new model is proposed to evaluate the nodes distribution uniformity by considering some parameters which include compression discrepancy, sparseness discrepancy, self discrepancy, maximum cavity radius and minimum cavity radius. The simulation results show that the presented model could be helpful for measuring the distribution uniformity of nodes scattered randomly in wireless sensor networks.

多机组核电厂机组间协作行为对组织可靠性的影响分析

“一址多堆”核电厂设计方案为核电的安全运营维护带来了新的挑战,组织人因失误是导致多机组核电厂事故的最常见的致因因素。目前学界针对多机组运行模式下的组织人因失误机理尚未形成统一的理论,相关分析方法也是在单机组背景下开发的,针对多机组核电厂机组间协作行为对组织可靠性的影响机理方面的研究不足。本文以Furuta提出的组织可靠性模型为基础,结合多机组场景下的组织行为特征,建立多机组核电厂组织可靠性数学网络模型;以双机组核电厂为例,模拟分析了机组间协作行为对多机组核电厂组织可靠性的影响。研究表明:机组间协作可以有效提升资源不足情况下的组织可靠性。相较于单机组独立运行,机组间协作对于组织内个别成员能力不足的容忍度更高。

Models and Methods for Urban Power Distribution Network Planning

The models, methods and their application experiences of a practical GIS(geographic information system)-based computer decision-making support system of urban power distribution network planning with seven subsystems,termed CNP,are described.In each subsystem there is at least one or one set of practical mathematical methobs.Some new models and mathematical methods have been introduced.In the development of CNP the idea of cognitive system engineering has been insisted on,which claims that human and computer intelligence should be combined together to solve the complex engineering problems cooperatively.Practical applications have shown that not only the optimal plan can be automatically reached with many complicated factors considered, but also the computation,analysis and graphic drawing burden can be released considerably.

Parallelized Jaccard-Based Learning Method and MapReduce Implementation for Mobile Devices Recognition from Massive Network Data

The ability of accurate and scalable mobile device recognition is critically important for mobile network operators and ISPs to understand their customers' behaviours and enhance their user experience.In this paper,we propose a novel method for mobile device model recognition by using statistical information derived from large amounts of mobile network traffic data.Specifically,we create a Jaccardbased coefficient measure method to identify a proper keyword representing each mobile device model from massive unstructured textual HTTP access logs.To handle the large amount of traffic data generated from large mobile networks,this method is designed as a set of parallel algorithms,and is implemented through the MapReduce framework which is a distributed parallel programming model with proven low-cost and high-efficiency features.Evaluations using real data sets show that our method can accurately recognise mobile client models while meeting the scalability and producer-independency requirements of large mobile network operators.Results show that a 91.5% accuracy rate is achieved for recognising mobile client models from 2 billion records,which is dramatically higher than existing solutions.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Evolutional Algorithm Based Cascade Long Reach Passive Optical Networks Planning

In this paper, we propose a mathe- matical model for long reach Passive Optical Networks （PON） planning. The model consid- ers the traffic demand, user requirements and physical constraints. It can support conven- tional star-like topologies as well as cascade PON networks. Then a two-stage evolutional algorithm is described to solve this problem. The first stage was to find a proper splitter can- didate site set, composing the outer loop. The second stage aimed to get the optimal topology when the splitter locations were selected, com- posing the internal loop. In this algorithm, the Pr/ifer sequence is used to build up a one-to-one correspondence between a PON network configuration and a chromosome. Compared with the results obtained by the enumeration method, the proposed model and algorithm are shown to be effective and accu- rate.

Research on BP-ANN Model of Semi-rigid Connection

The beam-to-column semirigid connection in a steel frame structure is represented by a zero-length rotational spring at the end of the beam element. The beam-to-column semirigid connection behavior is represented by its moment-rotation relationship. Several traditional mathematical models have been proposed to fit the moment-rotation curves from the experimental database,but they may be more reliable within certain ranges. In this paper, the intellectualized analytical model is proposed in the semirigid connections for top and seat angles with double web angles using the feed-forward back-propagation artificial neural network （BP-ANN） technique. the intellectualized analytical model from experimental results based on BP-ANN is more reliable and it is a better choice to the moment-rotation curves for beam-to-column semirigid connection. The results are found to provide effectiveness to the experimental response that is satisfactory for use in steel structural engineering design.

Cubature Formula for Spherical Basis Function Networks

Some mathematical models in geophysics and graphic processing need to compute integrals with scattered data on the sphere.Thus cubature formula plays an important role in computing these spherical integrals.This paper is devoted to establishing an exact positive cubature formula for spherical basis function networks.The authors give an existence proof of the exact positive cubature formula for spherical basis function networks,and prove that the cubature points needed in the cubature formula are not larger than the number of the scattered data.

Modeling an Evolving Complex Supply Network

Building a good supply network has a competitive advantage in every part of business. However, people rarely know the principles from which supply chain with complex organizational structure and function arise and develop. In this paper, we develop an evolving supply network model by using complex network theory. We mainly consider three kinds of firms＇ behaviors： entering of new firms, adding new relationships and rewiring of relationships among firms. By analyzing the statistical characteristics of the evolutionary dynamics of supply network, we find that the degree distribution follows a power-law distribution. Therefore, a supply network is a scale-free network where few but significant firms have lots of connections （called ＂hub＂ or core firm）, while most firms have few connections. These results are consistent with the results in empirical researches, which will be very useful for designing a robust and effective supply network.

RANDOM CONTINUOUS MODEL OF SCALE-FREE NETWORKS

There are a lot of continuous evolving networks in real world, such as Internet, WWW network, etc. The evolving operation of these networks are not an equating interval of time by chance. In this paper, the author proposes a new mathematical model for the mechanism of continuous single preferential attachment on the scale free networks, and counts the distribution of degree using stochastic analysis. Namely, the author has established the random continuous model of the network evolution of which counting process determines the operating number, and has proved that this system self-organizes into scale-free structures with scaling exponent γ=3＋a/m.