共查询到20条相似文献，搜索用时 405 毫秒
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运用网络位势理论、知识状态理论的分析方法,提出知识网络中知识状态演变机理问题。认为在关系位势和结构位势主导下,整合架构能力和知识存量水平对知识状态产生影响,并以此构建知识状态演变机理模型。通过案例研究,发现在不同网络位势作用下,知识状态呈现出离散型——收敛型——更高级离散型3个阶段的演化趋势。 相似文献
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动态系统的状态约束和控制约束等问题可归结为状态空间中某些集合的正不变性,它反映了系统族内部状态的具体性质,在研究轨线界限,过渡过程等方面具有独到的意义和作用.本文利用混合单调分解方法来研究离散时滞非线性凸多面体系统族的线性状态约束集合的鲁棒正不变性.对由矩阵凸多面体和区间扰动所描述的系统族,得到了鲁棒正不变集的充分条件,并给出了证明. 相似文献
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本文研究了设计与制造协同的内涵及工作模式,提出了一个支持设计与制造协同的设计状态成熟度模型,给出了设计状态成熟度模型的评价体系 相似文献
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相对资源承载力模型的改进及其实证分析 总被引：8，自引：2，他引：6
本文首先分析了相对资源承载力模型的不足,对其进行了四点改进:①在原模型基础上加入水和能源承载力;②为克服原模型中权重任意取值的不足,提出了基于优势资源牵引效应和劣势资源束缚效应原则下的相对综合承载力模型;③给出了新的承载状态划分标准;④进一步给出各状态下承载状态度标准。其次本文应用改进后的模型横向实证分析了2008年全国31个省及直辖市的可持续发展情况,以及从纵向上对新疆2000年-2007年的可持续发展情况进行了实证分析。最后本文对改进后的模型进行了评价,并提出了更一般化的理论模型。 相似文献
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结合分阶段投资的期权特性,分析了分阶段投资项目的价值构成;借鉴文献[1]的状态价格概念,提出了项目价值评估的状态价格二叉树基本模型;通过对某风险投资公司的投资项目分析,给出了具体实例的应用解释. 相似文献
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具有几何分布统计特征的在线租赁竞争分析 总被引：10，自引：3，他引：10
近年来,在线算法的兴起为金融领域的研究提供了新的视角,但传统的竞争分析方法有意规避概率分布假设。在金融领域中,似乎有时忽略这些极有价值的信息而只运用标准的竞争比方法分析显然是一个极大浪费。在本文中,我们首次结合输入结构的分布信息研究了离散型在线租赁问题,建立了最优的离散型在线租赁决策模型,并给出了最优的竞争策略及其竞争比。相比较Karp和El Yaniv的研究结果,由于本文引进了输入的分布信息使得竞争比改善;而相对于Fujiwara的研究结果,由于本文研究了离散型情形,给出了实际问题的精确解。 相似文献
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在网络化控制系统中引入离散隐马尔可夫模型,建立网络状态与控制器-执行器时延之间的概率模型.网络化控制系统被建模成一个马尔可夫跳变线性系统,并预测出当前采样周期内的控制器-执行器时延.使用该预测值设计一个状态反馈控制器,实现对控制器-执行器时延的补偿.对比仿真实验验证了所提方法的优越性. 相似文献
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《Information processing & management》2023,60(3):103328
Stock movement forecasting is usually formalized as a sequence prediction task based on time series data. Recently, more and more deep learning models are used to fit the dynamic stock time series with good nonlinear mapping ability, but not much of them attempt to unveil a market system’s internal dynamics. For instance, the driving force (state) behind the stock rise may be the company’s good profitability or concept marketing, and it is helpful to judge the future trend of the stock. To address this issue, we regard the explored pattern as an organic component of the hidden mechanism. Considering the effective hidden state discovery ability of the Hidden Markov Model (HMM), we aim to integrate it into the training process of the deep learning model. Specifically, we propose a deep learning framework called Hidden Markov Model-Attentive LSTM (HMM-ALSTM) to model stock time series data, which guides the hidden state learning of deep learning methods via the market’s pattern (learned by HMM) that generates time series data. What is more, a large number of experiments on 6 real-world data sets and 13 stock prediction baselines for predicting stock movement and return rate are implemented. Our proposed HMM-ALSTM achieves an average 10% improvement on all data sets compared to the best baseline. 相似文献
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提出了基于小波变换和隐马尔可夫模型的人像鉴别算法. 该算法首先对图像进行3级小波分解，然后把3个不同分辨率的低频子图像由小到大排列成树状结构，形成低频小波树. 接着利用独立元分析对每个小波树枝进行去相关、降维，形成特征小波树枝，并把它作为观测向量对隐马尔可夫模型进行训练，把优化的模型参数用于人脸识别. 分析了观测向量维数与识别率的关系，以及状态个数和高斯概率混合成分的个数对识别率的影响，定性描述了隐马尔可夫模型的本质. 在ORL人脸数据库上，同其他四种相关方法进行了比较，实验结果表明，该方法识别率较高，工程上易于应用. 相似文献
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In this paper, based on Stirling’?s polynomial interpolation formula, the Second-order Central Difference Predictive Filter (CDPF2) is proposed for nonlinear estimation. To facilitate the new method, the algorithm flow of CDPF2 is given first. Then, the theoretical deductions demonstrate that the estimated accuracy of the model error and system state for the CDPF2 is higher than that of the conventional PF. In addition, the stochastic boundedness and the error behavior of CDPF2 is analyzed for general nonlinear systems in a stochastic framework. The theoretical analysis presents that the estimation error will remain bounded and the covariance will remain stable if the system?s initial estimation error, disturbing noise terms and model error are small enough, which is the core part of the CDPF2 theory. All of the results have been demonstrated by numerical simulations for a nonlinear example system. 相似文献
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《Journal of The Franklin Institute》2023,360(4):3407-3435
In this paper, we study a distributed state estimation problem for Markov jump systems (MJS) over sensor networks, in which each sensor node connects with each other through wireless networks with communication delays. We assume that each sensor node maintains a buffer to store delayed data transmitted from neighbor nodes. A distributed multiple model filter is designed by using the interacting multiple model methods (IMM) and a recursive delays compensation method. In order to ensure the stability, two stability conditions are derived for boundedness of estimation errors and boundedness of error covariance. Finally, the effectiveness of the proposed methods is illustrated by simulations and experiments of maneuvering target tracking. 相似文献
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This paper deals with the state estimation of nonlinear discrete systems described by a multiple model with unknown inputs. The main goal concerns the simultaneous estimation of the system's state and the unknown inputs. This goal is achieved through the design of a multiple observer based on the elimination of the unknown inputs. It is shown that the observer gains are solutions of a set of linear matrix inequalities. After that, an unknown input estimation method is proposed. An academic example and an application dealing with message decoding illustrate the effectiveness of the proposed multiple observer. 相似文献
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This paper proposes an online video-based approach to handwritten Arabic alphabet recognition. Various temporal and spatial feature extraction techniques are introduced. The motion information of the hand movement is projected onto two static accumulated difference images according to the motion directionality. The temporal analysis is followed by two-dimensional discrete cosine transform and Zonal coding or Radon transformation and low pass filtering. The resulting feature vectors are time-independent thus can be classified by a simple classification technique such as K Nearest Neighbor (KNN). The solution is further enhanced by introducing the notion of superclasses where similar classes are grouped together for the purpose of multiresolutional classification. Experimental results indicate an impressive 99% recognition rate on user-dependant mode. To validate the proposed technique, we have conducted a series of experiments using Hidden Markov models (HMM), which is the classical way of classifying data with temporal dependencies. Experimental results revealed that the proposed feature extraction scheme combined with simple KNN yields superior results to those obtained by the classical HMM-based scheme. 相似文献
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【目的/意义】针对图书馆用户群体聚类分群不稳定且错误率较高的问题,提出基于马尔可夫模型的图书馆
用户聚类分群方法,提升图书馆用户聚类分群精准度。【方法/过程】采用一阶马尔可夫混合模型构建用户动作序列
模型,通过模型产生用户行为聚类,体现用户动作的动态性,采用自适应自然梯度算法,依据用户行为分离状态自
适应调整自身步长,优化模型参数学习中模型自动选择问题,实现最佳图书馆用户聚类分群。【结果/结论】通过实
验结果能够证明,实际聚类数量小于L值时,提出方法能够实现参数学习过程中模型的自动选择。提出方法的分群
数量最多,能够划分出最大的取值区间,聚类错误率最低为0.22%,聚类性能比较稳定,分群结果更加精准,达到了
设计的预期。【创新/局限】采用一阶马尔可夫混合模型实现了图书馆用户聚类分群。后续将进一步研究可考虑用
户序列间关联的高阶马尔可夫分量模型,以提高分群算法的准确性和稳定性。 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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In this paper, the state estimation problem for discrete-time networked systems with communication constraints and random packet dropouts is considered. The communication constraint is that, at each sampling instant, there is at most one of the various transmission nodes in the networked systems is allowed to access a shared communication channel, and then the received data are transmitted to a remote estimator to perform the estimation task. The channel accessing process of those transmission nodes is determined by a finite-state discrete-time Markov chain, and random packet dropouts in remote data transmission are modeled by a Bernoulli distributed white sequence. Using Bayes’ rule and some results developed in this study, two state estimation algorithms are proposed in the sense of minimum mean-square error. The first algorithm is optimal, which can exactly compute the minimum mean-square error estimate of system state. The second algorithm is a suboptimal algorithm obtained under a lot of Gaussian hypotheses. The proposed suboptimal algorithm is recursive and has time-independent complexity. Computer simulations are carried out to illustrate the performance of the proposed algorithms. 相似文献