共查询到20条相似文献，搜索用时 296 毫秒
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利用对角相似变换给出了一个求非负矩阵Perron根的迭代算法,并证明了其收敛性。该算法计算速度快,并用数值实例验证其迭代次数比文献[1]、[2]减少一半。 相似文献
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为提高电力数据调度效率,缩短电力数据调度延时,提出一种改进的无通信冲突的分布式电力数据聚集调度近似算法,采用Sink根数据聚集树对无线传感器网络中各个节点电力资源数据进行分层数据调度,根据分布式数据集对各个电力节点之间的控制信息进行不断融合处理,在最大独立集的基础上建立一棵根在Sink的数据聚集树。每个节点分配一个时间片,使该节点能在无通信冲突的情况下传输数据。仿真实验表明,采用改进算法得到的聚集延时明显减小,有效保证了电力调度控制的实时性,电力信息数据分层融合度能达到90%以上,而改进前的算法只有10%～50%之间。 相似文献
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研究了二维频域成像算法,提出一种基于RD域分块的改进距离徙动成像算法,并将该算法与近似算法和一般逆变标算法进行了点阵目标仿真和运算效率比较.当测绘带较宽且距离徙动较大时,该算法聚焦性能优于近似算法和一般逆变标算法.通过比较还可看出该算法运算效率高于逆变标算法.最后用实测数据仿真验证了该算法是可行的. 相似文献
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基于蚁堆原理的聚类算法是一种新的仿生算法,该算法已经在当前的数据挖掘研究中得到了多种应用。本文在介绍回顾该算法发展历史的基础上,简要评述了部分具有代表性的该算法改进模型及其应用情况。最后,对该算法在今后的研究方向作了展望。 相似文献
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介绍了基于SUSAN特征检测算子的图像边缘检测算法,分析了该算法原理及实现过程,并用Matlab实现该算法,给出了实验结果,分析了SUSAN算法的优势。 相似文献
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本文简述了字符串匹配算法的研究进展,分析了BF算法以及PRAM-CREW算法,并对该并行算法进行了改进,减少了处理器个数,使匹配次数大大减少,从而提高匹配效率。文章最后分析了该算法的性能。 相似文献
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提出了一种基于信息熵的可伸缩决策树生成算法SDT(AAScalableDecisionTreeAlgorithm)。与SPRINT算法不同,该算法使用基于信息熵增益的思想分割训练样本集,引入了新的数据结构:基于类别的属性表。该表记录存储了计算分割属性的所有信息,并且该表的大小不会随样本集的增大而增大,可以常贮主存。与SLIQ算法相比,SDTA算法彻底摆脱了主存容量对算法效率的限制。实验表明,SDTA算法能生成正确的决策树,而且具有良好的可伸缩性。 相似文献
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随机森林算法在数据挖掘领域中得到了广泛的应用,该算法通过构建多个不同的决策树可以获得更高的分类结果。但是,随着数据规模的增大,人们开始接触到各大规模的数据以及更高维度的数据属性。传统的随机森林构建算法不能有效、快速地处理海量高维数据,严重影响了数据的分类效率,从而影响预测效率。本文针对高维、海量数据下随机森林构建算法,改进并提高了该算法的效率,提出了基于云计算平台的随机森林构建算法。该算法可以快速的完成数据分类预测,并通过实验结果进一步展示了该算法的效率以及可扩展性。 相似文献
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为了提高人脸检测的速度及鲁棒性,提出了一种基于级联分类器和期望最大、主成分分析（ＥＭ ＰＣＡ）的人脸检测方法．该方法在训练阶段利用不同分辨率的训练样本来训练２个ｆｉｓｈｅｒ线性分类器,再利用ＥＭ ＰＣＡ提取特征来训练非线性支持向量机（ＳＶＭ）;在检测阶段,首先通过２个ｆｉｓｈｅｒ线性分类器快速过滤掉大量的背景区域,再利用非线性支持向量机对余下的候选区域进行进一步验证,以确认是否为人脸．实验结果证明了该方法的有效性和正确性． 相似文献
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基于Mohr-Coulomb破坏准则下材料的非线性特征,结合粘塑性应力空间内破坏准则被有限超越期间的稳定时间步长,对随机数学覆盖下粘塑性数值算法的逻辑实现过程进行推导,在直接偏微分理论基础上建立了三维及平面应变条件下粘塑性非线性随机有限元的本构关系式,进而提出了基于全量理论的粘塑性非线性随机有限元列式,并以堤防填筑工程为例,分析研究了土质堤坝分阶段逐步填筑过程中的随机演化机理及堤坝结构的可靠度安全性,实现了堤防填筑工程的全程随机模拟。 相似文献
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This paper considers the problem of the leader-following consensus of generally nonlinear discrete-time multi-agent systems with limited communication channel capacity over directed fixed communication networks. The leader agent and all follower agents are with multi-dimensional nonlinear dynamics. We propose a novel kind of consensus algorithm for each follower agent based on dynamic encoding and decoding algorithms and conduct a rigorous analysis for consensus convergence. It is proved that under the consensus algorithm designed, the leader-following consensus is achievable and the quantizers equipped for the multi-agent systems can never be saturated. Furthermore, we give the explicit forms of the data transmission rate for the connected communication channel. By properly designing the system parameters according to restriction conditions, we can ensure the consensus and communication efficiency with merely one bit information exchanging between each pair of adjacent agents per step. Finally, simulation example is presented to verify the validity of results obtained. 相似文献
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《Journal of The Franklin Institute》2023,360(12):8753-8771
In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm. 相似文献
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Najmeh Daroogheh Nader Meskin Khashayar Khorasani 《Journal of The Franklin Institute》2018,355(8):3753-3794
Health monitoring of nonlinear systems is broadly concerned with the system health tracking and its prediction to future time horizons. Estimation and prediction schemes constitute as principle components of any health monitoring technique. Particle filter (PF) represents a powerful tool for performing state and parameter estimation as well as prediction of nonlinear dynamical systems. Estimation of the system parameters along with the states can yield an up-to-date and reliable model that can be used for long-term prediction problems through utilization of particle filters. This feature enables one to deal with uncertainty issues in the resulting prediction step as the time horizon is extended. Towards this end, this paper presents an improved method to achieve uncertainty management for long-term prediction of nonlinear systems by using particle filters. In our proposed approach, an observation forecasting scheme is developed to extend the system observation profiles (as time-series) to future time horizon. Particles are then propagated to future time instants according to a resampling algorithm instead of considering constant weights for the particles propagation in the prediction step. The uncertainty in the long-term prediction of the system states and parameters are managed by utilizing dynamic linear models for development of an observation forecasting scheme. This task is addressed through an outer adjustment loop for adaptively changing the sliding observation injection window based on the Mahalanobis distance criterion. Our proposed approach is then applied to predicting the health condition as well as the remaining useful life (RUL) of a gas turbine engine that is affected by degradations in the system health parameters. Extensive simulation and case studies are conducted to demonstrate and illustrate the capabilities and performance characteristics of our proposed and developed schemes. 相似文献
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In this paper, the distributed optimization problem over multi-cluster networks is considered. Different from the existing works, this paper studies the optimization algorithm under uncoordinated step sizes. More specifically, by combining a random sleep strategy and the round-robin communication among clusters, a new hierarchical algorithm is developed to solve the considered problem. In the proposed algorithm, the random sleep strategy enables each agent to independently choose either performing the projected subgradient descent, or keeping the previous estimate by a Bernoulli decision, based on which the step size of each agent is selected as an uncoordinated form that only relates to the independent Bernoulli decision variable. Technically, by introducing a key definition on the algorithm history, it is proven that the estimates of the proposed algorithm can converge to the optimal solution even with the uncoordinated step sizes. In addition, we also study the convergence performance of the proposed algorithm with simpler constant step sizes. In this case, it is proven that the random sleep strategy can efficiently improve the convergence accuracy of the algorithm. Finally, the theoretical findings are verified via simulation examples. 相似文献
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A solution of the periodic problem in a nonlinear system comprising a single-valued symmetric nonlinearity (Lure system) and linear dynamics is presented. The solution is designed as an iterative application of Periodic Signal Mapping to refine the approximate solution obtained through the describing function method. The algorithm is based upon the transformation of the original nonlinear system into an equivalent nonlinear system for which the filtering hypothesis is satisfied exactly, and for that reason the latter being suitable for application of the developed algorithm. The solution sought for is a fixed point of the periodic signal mapping. Conditions of asymptotic convergence of the proposed algorithm are given. The proposed approach is illustrated by two examples of analysis of periodic motions in a relay feedback system and chattering in a terminal sliding mode. 相似文献
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为了提高高维数据集合离群数据挖掘效率,在分析了传统的离群数据挖掘算法优点和缺点的基础上,提出了一种离群点检测算法,首先将非线性问题转化为高维特征空间中的线性问题,然后利用非线性数据变换进行维数约减,对从高维采样数据中恢复得到低维数据集,通过本文提出的离群数据假设,并结合本文给出的离群聚类方法对所得数据对象投影分量是否是离群数据进行判别。仿真实验的结果表明了该方法能够有效地发现高维数据集中的离群点。与此同时,该算法具有参数估计简单、参数影响不大等优点,为离群点检测问题的机器学习提供了一条新的途径。 相似文献