基于粗集和神经网络的煤炭资源资产分类研究

准确的煤炭资源资产分类是有效进行煤炭资源资产管理的前提和基础.针对现有分类方法存在的不足,提出了基于人工神经网络(ANN)与粗集理论(RS)的煤炭资源资产分类方法.首先,由资产分类的样本数据形成决策表,使用专家离散法对数据进行离散处理;然后,采用遗传算法(GA)对决策表进行属性约简;最后根据约简后的属性集构建起煤炭资源资产分类的神经网络模型.实例运行表明,所提出的模型方法比单纯的ANN方法在学习效率和分类准确率方面均有所提高.     

基于支持向量机的神经元形态分类

针对神经元的空间几何形态特征分类问题以及神经元的生长预测问题进行了探讨.结合神经元的形态数据,分别建立了基于支持向量机的神经元形态分类模型、基于主成分分析和支持向量机的神经元分类模型以及基于遗传算法和RBF网络的神经元生长预测模型,在较合理的假设下,对各个模型进行求解,得到了较理想的结果.     

基于银行-资产二分网络的银行间风险传染研究

银行之间越来越紧密的联系形成了复杂的网络,使得风险能够在网络中迅速传染,造成大规模的级联失效.目前已有的大多数研究都基于银行间资产负债表关联来分析银行间风险传染,而银行间持有相同资产的关联作为一种重要的银行间风险传染的渠道,往往没有引起足够的重视.基于持有相同资产关联,建立银行-资产二分网络研究银行间风险传染的机理特征.首先设计了风险传染模型,接着收集了244家主要银行2016年的资产价值数据作为模型仿真的基础.仿真结果表明银行间持有相同资产关联会造成银行间风险传染,继而引发大规模的银行级联破产,其危害程度和传染速度随着冲击大小和溢出效应强度的增加而增加.同时,按照资产占比将244家银行聚成5类并分析各类银行的风险特点.分析对监管部门进行合理分配监管资源具有一定的参考意义.     

融合注意力机制的残差网络晶体硅片分类方法

为了更好地利用晶体硅片资源,实现对晶体硅片准确高效的分类,提出了一种改进的ResNet34卷积神经网络,且用于对晶体硅片高清图像进行分类.通过拍摄晶体硅片高清图像建立自有数据集,并对其进行离线扩充来有效扩大数据集.基于ResNet34网络建立分类模型,采取自适应矩估计权重衰减优化算法(AdamW)来提高ResNet34网络的泛化能力,同时将注意力机制的方法融入到ResNet34网络中增强模型的特征提取能力,之后将改进的模型载入到晶体硅片数据集上训练,实验结果发现,所提W-ResNet34+SC-SEAM分类模型的准确率可达99.91%,比在仅利用ResNet34模型分类结果上提高了2.68%的准确率,实现了对晶体硅片的精确分类,证明了所提分类方法是可行的.     

基于BP算法的信用风险评价模型研究

本文利用神经网络技术建立基于 BP算法的信用风险评价模型 ,为我国某商业银行 12 0家贷款企业进行信用风险评价 ,按照企业的信用等级分为“信用好”、“信用中等”和“信用差”三个小组 .仿真结果表明 ,本文所建立的神经网络信用风险评价模型的分类准确率高于传统的参数统计分类方法——线性判别分析法的分类准确率 .文中还详细给出神经网络信用风险评价模型的网络构建方法及基于 BP网络的学习算法和步骤 .     

MEA-BP模型在遥感影像分类中的应用研究

遥感影像分类作为遥感技术的一个重要应用,对遥感技术的发展具有重要作用.针对遥感影像数据特点,在目前的非线性研究方法中主要用到的是BP神经网络模型.但是BP神经网络模型存在对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.因此,为了提高模型遥感影像分类精度,提出采用MEA-BP模型进行遥感影像数据分类.首先采用思维进化算法代替BP神经网络算法进行初始寻优,再用改进BP算法对优化的网络模型权阈值进一步精确优化,随后建立基于思维进化算法的BP神经网络分类模型,并将其应用到遥感影像数据分类研究中.仿真结果表明,新模型有效提高了遥感影像分类准确性,为遥感影像分类提出了一种新的方法,具有广泛研究价值.     

基于邻域粗糙集的网络入侵分类诊断组合模型研究

网络入侵诊断直接影响网络正常运行和安全.针对入侵类型复杂,现有分类诊断模型精度有限的问题,提出一种基于邻域粗糙集的网络入侵分类诊断优化模型.首先,运用邻域粗糙集对网络入侵数据进行条件属性的约简,确定关键属性,然后将其作为训练输入构建相关向量机分类诊断模型,并同时运用遗传算法进行超参数优化,提高模型诊断精度和速度.通过KDDCup99数据集对优化模型性能进行检验,结果表明,组合预测方法精确度高于支持向量机、相关向量机和BP神经网络.组合模型诊断精度高、速度快,具有优异的综合性能.     

带最小交易单位的Markowitz模型及算法

在证券交易市场中,交易规则要求购买的股票数量为整数.基于这种情况,将Markowitz模型中资产的投资比例改进为资产的投资数量,构造了一个二次整数规划模型.设计了求解该模型的算法,经过实证分析,算法是有效的.     

基于最大熵和最小交叉熵方法的上证50ETF期权定价

基于最大熵方法和最小交叉熵方法,给出了根据期权价格推断标的资产价格分布的模型和已知先验信息下推断标的资产价格分布的模型,利用拉格朗日乘子法给出了模型的简化解,通过粒子群算法求出标的资产价格的密度函数,进而对上证50ETF期权进行定价比较.实证结果表明:基于最大熵方法推断的分布可以作为标的资产价格分布的较好估计;基于最小交叉熵方法推断的分布是在先验信息下标的资产价格分布的较好估计,两种方法适用于我国上证50ETF期权定价.     

数据分割的分子动力学算法

数据分割研究的基本内容是数据的分类和聚类,是数据挖掘的核心问题之一,在实际问题中应用广泛.特别是针对有向网络数据的研究更是学科发展的前沿.但由于这类问题结构的非对称性,使得模型与算法的构建存在本质困难,因此相应的研究结果较少.本文借鉴分子动力学方法的思想,提出了一类新的网络数据半监督分类模型及算法.该算法不仅适用于关系对称的无向网络数据,而且适用于关系非对称的有向网络.最后针对期刊引用网络数据进行了数值实验,结果表明了模型及算法的可行性和有效性.     

MULTIQ: A Queueing Model for FMSs with Several Pallet Types

Analytical modelling of the work flow through flexible manufacturing systems (FMSs), based on closed queueing network models, has been successfully applied to the early stages of design and analysis of FMSs. This paper describes the advantages of using multiple job-class closed queueing networks for modelling realistic situations occurring in FMSs. The general modelling of FMSs by closed queueing networks is first reviewed. The way Solberg's CAN-Q—a single job-class queueing-based package—deals with several part types is clarified. A new model called MULTIQ, allowing multiple pallet types, each of which is used by several part types, is proposed. Results are derived using the data from an existing FMS. The use of the MULTIQ model for optimization purposes is suggested by some examples.     

Logistic evolutionary product-unit neural networks: Innovation capacity of poor Guatemalan households

A new logistic regression algorithm based on evolutionary product-unit (PU) neural networks is used in this paper to determine the assets that influence the decision of poor households with respect to the cultivation of non-traditional crops (NTC) in the Guatemalan Highlands. In order to evaluate high-order covariate interactions, PUs were considered to be independent variables in product-unit neural networks (PUNN) analysing two different models either including the initial covariates (logistic regression by the product-unit and initial covariate model) or not (logistic regression by the product-unit model). Our results were compared with those obtained using a standard logistic regression model and allow us to interpret the most relevant household assets and their complex interactions when adopting NTC, in order to aid in the design of rural policies.     

A new perspective on the normalization of invariant measures for loss networks and other product form systems

Loss networks have proved to be a vital tool in the study of telecommunication networks, computer performance, and resource allocation problems. For a large subset of these models, the invariant measure is of a product form. The existence of efficient procedures to normalize a product-form invariant measure is essential for analysis of the underlying system.Choudhury et al. [1—4] have recently presented a number of algorithms based upon Fourier analysis for the normalization of product-form invariant measures for specific systems. Bean and Stewart [5] discussed related work for the normalization of product-form invariant measures for closed queueing networks. In this paper, we present a simple unifying framework within which to study these algorithms. This framework is significant as it suggests a number of extensions to these algorithms and simplifies their derivation.     

GAME-THEORETIC RESOURCE MODELING

Games—mathematical models of strategic interaction—embrace a multitude of disciplines, in each of which they form a largely separate field of study with its own traditions and priorities. Game-theoretic resource modeling concerns itself primarily with strategic aspects of externalities arising from joint use of the commons, although it also deals with externalities of privately owned resources and with questions of fairness in allocating shared resource costs. This essay surveys resource games—in fisheries, forestry, water resources and environmental regulation—within a novel unifying framework, assesses their contribution to current understanding of resource issues, and indicates their promise for the future.     

基于LSMC模型的煤炭资源价值定价研究

煤炭资源价值定价可以抽象为一种美式期权定价问题.最小二乘蒙特卡洛模拟(LSMC)方法是解决美式期权定价问题的一个有效途径.详尽地分析了Cortazar等人的基于资源价格、利率和便利收益随机变动的三因素定价模型,利用向量Ito定理提出了三因素模型中价格、利率和便利收益变量的递推公式.对LSMC方法原理进行了细致的阐述,总结出实现LSMC方法的完整过程,并在Matlab环境下编制了LSMC算法实现程序,进行算例计算.算例结果表明,LSMC方法用于资源定价是有效可靠的.研究为煤炭资源价值定价提供了一个完整具有可操作性的工具.     

DG method for numerical pricing of multi-asset Asian options—The case of options with floating strike

Option pricing models are an important part of financial markets worldwide. The PDE formulation of these models leads to analytical solutions only under very strong simplifications. For more general models the option price needs to be evaluated by numerical techniques. First, based on an ideal pure diffusion process for two risky asset prices with an additional path-dependent variable for continuous arithmetic average, we present a general form of PDE for pricing of Asian option contracts on two assets. Further, we focus only on one subclass—Asian options with floating strike—and introduce the concept of the dimensionality reduction with respect to the payoff leading to PDE with two spatial variables. Then the numerical option pricing scheme arising from the discontinuous Galerkin method is developed and some theoretical results are also mentioned. Finally, the aforementioned model is supplemented with numerical results on real market data.     

Structure and logic in knowledge-based representations of systems simulation models

The paper focuses on the similarity between modelling and knowledge representation, trying to bring together the OR/Systems Science and the Artificial Intelligence views when referring to a computer system simulation, especially of the discrete-event or the network types. The models we consider are generalized activity networks with resources, including either models with a finite lifetime, such as project scheduling networks, or steady state models, such as queueing networks. By enhancing the structure of entities and states and the logic of transitions within a model specification, modularity is improved and one may adopt a more declarative approach. The relational and rule-based representation formalisms are a convenient choice for that purpose. Then, the use of knowledge bases both for the static (i.e. consultative) and the dynamic (i.e. experimental) study of the model turns up to be more natural. Moreover, the task of building an expert system for decision support on system analysis or synthesis becomes easier. The paper reports some original work in the above directions, using a logic programming approach and an associated specification methodology based on general systems concepts.     

Resource pricing games on graphs: existence of Nash equilibria

In this letter, we consider a non-cooperative resource pricing game on a graph where sellers (i.e., players) set the prices for their own resources to maximize the payoffs and buyers migrate to seek the least expensive resources. We present a model for the resource pricing game and prove the existence of Nash equilibria on regular and hierarchical graphs. The results obtained are applicable to the study of market economies, social networks and computer networks where individuals trade resources in a spatially extended environment.     

A CS-AdaBoost-BP model for product quality inspection

Quality inspection is essential in preventing defective products from entering the market. Due to the typically low percentage of defective products, it is generally challenging to detect them using algorithms that aim for the overall classification accuracy. To help solve this problem, we propose an ensemble learning classification model, where we employ adaptive boosting (AdaBoost) to cascade multiple backpropagation (BP) neural networks. Furthermore, cost-sensitive (CS) learning is introduced to adjust the loss function of the basic classifier of the BP neural network. For clarity, this model is called a CS-AdaBoost-BP model. To empirically verify its effectiveness, we use data from home appliance production lines from Bosch. We carry out tenfold cross-validation to evaluate and compare the performance between the CS-AdaBoost-BP model and three existing models: BP neural network, BP neural network based on sampling, and AdaBoost-BP. The results show that our proposed model not only performs better than the other models but also significantly improves the ability to identify defective products. Furthermore, based on the mean value of the Youden index, our proposed model has the highest stability.