共查询到20条相似文献，搜索用时 218 毫秒
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选址问题目前学术界已有较多的研究成果,但大多数是将总费用作为目标函数,一般要求事先给出网络结点的位置坐标,且无需考虑结点间的最短路程,旨在确定新的地理几何中心。而对已有网络,在不改变原有路径及各结点位置的条件下,以总路程最小为目标函数,在现有网络结点中寻找其中某些结点的最优位置却是一个新的研究课题。本文以某高校校园卡充值点为例,将校园示意图转化为赋权连通图,求得该连通图的邻接矩阵,利用Floyd算法及图论软件包构造一个最短路径矩阵,得到一个赋权完全图,利用穷举法或混合整数规划法及数学软件求解,得到各学院、楼栋、学生宿舍区到三个校园卡充值点的最短总路程及三个校园卡充值点的最优位置。 相似文献
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整箱货物的混合装箱问题在采用传统的算法时,当货物种类规模较大,通常会出现计算时间的"维数灾难"。而遗传算法在搜索过程中不容易陷于局部最优,并能以很大的概率找到整体最优解,具有较快的全局最优解的求解能力。本文以一实例说明了遗传算法在求解混合装箱问题的适用性。 相似文献
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对两参数随机过程,可以定义多种不同的马氏性,这些马氏性之间的关系一直是人们十分关注的问题,许多学者对此进行了研究,取得了许多重要的结果。但是,至今仍有一些问题没有得到解决,这些问题是:1.单点马氏性是否蕴含弱的宽过去马氏性、L—马氏性、宽Levy马氏性;2.宽Levy马氏性是否蕴含单点马氏性、弱的宽过去马氏性、Levy马氏性;3.Levy马氏性是否蕴含单点马氏性;4.*-马氏性是否蕴含宽Levy马氏性。本文解决了前三个问题,从而得到了一个比较完美的各种两参数马氏性间的关系图。 相似文献
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本文介绍了时间离散且状态可数的Markov过程——马氏链,阐述了马氏链在教学满意度分析中的应用的基本思想。利用历年的教学满意度建立一个马氏链,构建转移概率矩阵,通过适当处理,提炼出有用信息,为指导教师提高教学质量提供理论依据。 相似文献
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评价引文系统里一个引文元素在系统中的地位,是引文系统定量研究的课题。已建立的评价系统的数学模型,从有向图模型,推广到有向赋权图模型。应用有向赋权图表达复杂的引文系统,在此基础上讨论了引文系统情报元地位模型的存在条件,证明了本模型的地位指数向量就是引文系统的有向赋权图权矩阵的最大特征向量。 相似文献
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双曲方程的稳定解分析方法在现代数学应用中具有广泛的意义。采用时齐马氏链进行双曲方程稳定解存在性分析具有模型匹配度高的优点。构建时齐马氏链的双曲波动方程,设计自组织非光滑时滞的双曲系统,结合时齐马氏链渐进性条件下的Lyapunov-Krasovskii泛函算法,对时齐马氏链渐进性条件临界阈值确定,以有效分析双曲方程的稳定解存在性,提高并行算法处理效率,在一阶非光滑时滞系统中得到方向性时齐马氏链函数的分解特征。研究证明,时齐马氏链渐进性条件下,双曲方程存在稳定性解,解向量在有限时间内收敛。 相似文献
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信息系统投资的最优组合赋权法研究 总被引：2，自引：0，他引：2
文章针对信息系统项目投资方案决策中属性权重的确定问题，给出了一种主、客观最优组合赋权法，即将主观权重与客观权重加权综合，并通过数学规划模型求出各自的加权系数。 相似文献
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《内蒙古科技与经济》2016,(11)
马氏文化资源库的建设构想,引出对马氏文献资源的特征的分析及数据库的建设规划。构建过程中,针对文献特点出台整理方案,制定资源库元数据录入原则、元数据编目细则及数字化加工流程。实践的过程也引发出关于在今后建设特色数据库时,如何体现地方历史文献来源的特色和如何运用专业方式整理文献等问题的思考。 相似文献
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《Information processing & management》2019,56(6):102093
Knowledge graphs are widely used in retrieval systems, question answering systems (QA), hypothesis generation systems, etc. Representation learning provides a way to mine knowledge graphs to detect missing relations; and translation-based embedding models are a popular form of representation model. Shortcomings of translation-based models however, limits their practicability as knowledge completion algorithms. The proposed model helps to address some of these shortcomings.The similarity between graph structural features of two entities was found to be correlated to the relations of those entities. This correlation can help to solve the problem caused by unbalanced relations and reciprocal relations. We used Node2vec, a graph embedding algorithm, to represent information related to an entity's graph structure, and we introduce a cascade model to incorporate graph embedding with knowledge embedding into a unified framework. The cascade model first refines feature representation in the first two stages (Local Optimization Stage), and then uses backward propagation to optimize parameters of all the stages (Global Optimization Stage). This helps to enhance the knowledge representation of existing translation-based algorithms by taking into account both semantic features and graph features and fusing them to extract more useful information. Besides, different cascade structures are designed to find the optimal solution to the problem of knowledge inference and retrieval.The proposed model was verified using three mainstream knowledge graphs: WIN18, FB15K and BioChem. Experimental results were validated using the hit@10 rate entity prediction task. The proposed model performed better than TransE, giving an average improvement of 2.7% on WN18, 2.3% on FB15k and 28% on BioChem. Improvements were particularly marked where there were problems with unbalanced relations and reciprocal relations. Furthermore, the stepwise-cascade structure is proved to be more effective and significantly outperforms other baselines. 相似文献
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Motivated by the wide application of Markov-chain steady-state-probability estimation, we pursue a spectral and graph-theoretic performance analysis of a classical estimator for steady-state probabilities here. Specifically, we connect a performance measure to estimate the structure of the underlying graph defined on the Markov-chain's state transitions. To do so, (1) we present a series of upper bounds on the performance measure in terms of the subdominant eigenvalue of the state transition matrix, which is closely connected with the graph structure; (2) as an illustration of the graph-theoretic analysis, we then relate the subdominant eigenvalue to the connectivity of the graph, including for the strong-connectivity case and the weak-link case. We also apply the results to characterize estimation in Markov chains with rewards. 相似文献
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《Journal of The Franklin Institute》2023,360(12):9084-9095
This paper aims to investigate the mean square consensus (MSC) problem of a class of nonlinear networked systems subject to directed and stochastic switching communication topologies, where the switching law is determined by an ergodic continuous-time Markov process. The cooperative consensus controller is designed by using an observer-based method. Firstly, for the case with Lur’e nonlinear dynamics, by developing a stochastic Lyapunov function, we show that the MSC under consideration can be realized if the union of the underlying network graphs has a directed spanning tree. It is worth noting that none of the network graphs is required to contain a directed spanning tree. Moreover, we study the MSC problem for networked systems with Lipschitz-type nonlinear dynamics. Finally, a numerical simulation is conducted on multiple Chua’s circuit systems to illustrate the effectiveness of the proposed controllers. 相似文献
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This paper deals with the problem of finding graphs (directed and undirected) maximizing the number of spanning trees among the regular graphs with the same number of nodes and edges. The approach is based on heuristic algorithms such as k-optimal and evolutionary. The emphasis is rather on checking whether these techniques are applicable to solving extremal graph problems than investigating generic structures of optimal graphs. For this reason circulant graphs, for which computationally effective tree counting formulas exist, are discussed first and then the results extended to cover the class of regular graphs. 相似文献
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Hong Zhu Chuan Chen Li-Zhi Liao Michael K. Ng 《Journal of The Franklin Institute》2018,355(4):1819-1845
The core issue of multiple graphs clustering is to find clusters of vertices from graphs such that these clusters are well-separated in each graph and clusters are consistent across different graphs. The problem can be formulated as a multiple orthogonality constrained optimization model which can be shown to be a relaxation of a multiple graphs cut problem. The resulting optimization problem can be solved by a gradient flow iterative method. The convergence of the proposed iterative scheme can be established. Numerical examples are presented to demonstrate the effectiveness of the proposed method for solving multiple graphs clustering problems in terms of clustering accuracy and computational efficiency. 相似文献
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Fuzzy-model-based tracking control of Markov jump nonlinear systems with incomplete mode information
Dan Cui Yue Wang Hongye Su Zhaowen Xu Haoyi Que 《Journal of The Franklin Institute》2021,358(7):3633-3650
This paper considers the tracking control problem for nonlinear Markov jump systems based on T–S fuzzy model approach with incomplete mode information. It is assumed that the mode transition rate matrix is not a priori knowledge and only partial information is available. Moreover, the mode where the system stays when operating is not fully accessible to the designed controller. In this incomplete mode information scenario, a hidden Markov model based mechanism is modified to simulate the mode deficiency mapping. The incomplete transition rate matrix is well defined in the form of a polynomial. Based on this, by constructing a polynomially parameter-dependent Lyapunov matrices and linear matrix techniques, sufficient conditions are established to ensure the stochastic stability and a prescribed tracking performance. The controller design scheme are presented by solving a series of LMIs. Examples are given in the end to illustrate the effectiveness of our proposed results. 相似文献
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《Journal of The Franklin Institute》2023,360(12):7808-7831
This paper solves the finite-time consensus problem for discrete time multi-agent systems (MASs) where agents update their values via linear iteration and the interactions between them are described by signed digraphs. A sufficient condition is presented that the agents can reach consensus on any given linear function of multiple initial signals in finite time, i.e., there exists an eventually positive Laplacian-based matrix associated with the underlying graph. We prove that the linear iterative framework “ratio consensus” developed for unsigned graphs in the literature can be extended to the computation for signed graphs with appropriate modifications. Our method weakens the limitation of the iterative framework on the “marginal Schur stability” of the weight matrix without increasing the computational complexity. Reaching average consensus on unsigned graphs as in the literature is regarded as a special case of our algorithm. Two illustrative examples are presented to demonstrate the correctness of the proposed results. 相似文献
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Research on clustering algorithms in synonymy graphs of a single language yields promising results, however, this idea is not yet explored in a multilingual setting. Nevertheless, moving the problem to a multilingual translation graph enables the use of more clues and techniques not possible in a monolingual synonymy graph. This article explores the potential of sense induction methods in a massively multilingual translation graph. For this purpose, the performance of graph clustering methods in synset detection are investigated. In the context of translation graphs, the use of existing Wordnets in different languages is an important clue for synset detection which cannot be utilized in a monolingual setting. Casting the problem into an unsupervised synset expansion task rather than a clustering or community detection task improves the results substantially. Furthermore, instead of a greedy unsupervised expansion algorithm guided by heuristics, we devise a supervised learning algorithm able to learn synset expansion patterns from the words in existing Wordnets to achieve superior results. As the training data is formed of already existing Wordnets, as opposed to previous work, manual labeling is not required. To evaluate our methods, Wordnets for Slovenian, Persian, German and Russian are built from scratch and compared to their manually built Wordnets or labeled test-sets. Results reveal a clear improvement over 2 state-of-the-art algorithms targeting massively multilingual Wordnets and competitive results with Wordnet construction methods targeting a single language. The system is able to produce Wordnets from scratch with a Wordnet base concept coverage ranging from 20% to 88% for 51 languages and expands existing Wordnets up to 30%. 相似文献
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《Information processing & management》2023,60(3):103297
Humans are able to reason from multiple sources to arrive at the correct answer. In the context of Multiple Choice Question Answering (MCQA), knowledge graphs can provide subgraphs based on different combinations of questions and answers, mimicking the way humans find answers. However, current research mainly focuses on independent reasoning on a single graph for each question–answer pair, lacking the ability for joint reasoning among all answer candidates. In this paper, we propose a novel method KMSQA, which leverages multiple subgraphs from the large knowledge graph ConceptNet to model the comprehensive reasoning process. We further encode the knowledge graphs with shared Graph Neural Networks (GNNs) and perform joint reasoning across multiple subgraphs. We evaluate our model on two common datasets: CommonsenseQA (CSQA) and OpenBookQA (OBQA). Our method achieves an exact match score of 74.53% on CSQA and 71.80% on OBQA, outperforming all eight baselines. 相似文献