基于局部子图匹配的SLAM 方法

针对现有的SLAM 解决方法在机器人被“绑架”时失效的问题,提出了基于局部子图匹配的方法．该 方法对现有的SLAM 解决构架进行了改进,提出交点最优匹配的特征相关算法,并且将奇异值分解方法引入机器人 定位．最后,在结构化环境下将本方法和基于扩展卡尔曼滤波器的方法进行比较,讨论了基于局部子图匹配的方法 在结构化环境中解决机器人“绑架”问题的有效性和可行性．     

基于细胞因子网络协同进化的Web服务合成方法

借鉴生物神经内分泌免疫系统的协同进化机制,基于细胞因子网络平台提出了Web服务的合成方法.在细胞因子网络调控下的生物实体代理Web服务,构成为一个带有条件的米兰机单元,Web服务的合成可以转换为米兰机进化过程.生物实体通过消息匹配和条件约束形成细胞因子网络,Web服务的合成通过生物实体细胞因子网络支持.在服务协同进化过程中可以动态调整其合成的服务,完成服务的动态自组织合成和管理等工作.仿真结果表明该方法在环境动态变化时具有适应性.     

一种含影响力因子的自适应C均值算法

含影响力因子的硬聚类算法(HCMef)在对多于两类规模不均的样本进行聚类时,影响力因子指数对聚类结果影响很大,取值不当会引起类消失.影响力因子指数在0附近穷举,并利用影响力因子指数较大时类规模会在较少训练次数内消失的现象,降低计算量.提出了含影响力因子的自适应C均值聚类策略(AHCMef),并介绍了两阶段聚类方法,进一步提高聚类成功率和执行效率.结果显示,最佳影响力因子指数出现在成功聚类时的较大值附近,聚类效果从该点起随影响力因子指数的减小而降低.对HCMef算法应用于多类规模不均样本情况时,影响力因子指数的选取具有指导意义.     

序列模式挖掘研究与发展

序列模式挖掘是数据挖掘的一个重要研究课题,它在很多领域中都有着广泛的应用.首先讨论了序列模式挖掘的相关背景,然后对序列模式挖掘进行分类,并在此基础上对每一类序列模式挖掘算法的特点进行了介绍和比较;最后,对序列模式挖掘未来的研究重点进行展望,以便研究者对序列模式挖掘做进一步的研究.     

引入趋近律的功率因数校正滑模控制仿真研究

提出一种引入趋近律的滑模变结构控制(SMVSC)方法来实现有源功率因数校正(APFC),减少电流的谐波成分.SMVSC是一种解决非线性时变系统(如APFC系统)问题的良好办法,但是实际应用中SMVSC的"抖振"现象问题必须要解决好.引入趋近律来削弱APFC系统在滑模控制中的"抖振"现象,并且依此推导出了控制APFC系统中的功率开关的PWM占空比.采用Matlab平台进行仿真验证控制策略控制APFC电路几乎得到单位功率因数,同时超调减小、响应时间缩短.     

Approximate dynamic programming solutions with a single network adaptive critic for a class of nonlinear systems

Approximate dynamic programming (ADP) formulation implemented with an adaptive critic (AC)-based neural network (NN) structure has evolved as a powerful technique for solving the Hamilton-Jacobi-Bellman (HJB) equations. As interest in ADP and the AC solutions are escalating with time, there is a dire need to consider possible enabling factors for their implementations. A typical AC structure consists of two interacting NNs, which is computationally expensive. In this paper, a new architecture, called the ‘cost-function-based single network adaptive critic (J-SNAC)’ is presented, which eliminates one of the networks in a typical AC structure. This approach is applicable to a wide class of nonlinear systems in engineering. In order to demonstrate the benefits and the control synthesis with the J-SNAC, two problems have been solved with the AC and the J-SNAC approaches. Results are presented, which show savings of about 50% of the computational costs by J-SNAC while having the same accuracy levels of the dual network structure in solving for optimal control. Furthermore, convergence of the J-SNAC iterations, which reduces to a least-squares problem, is discussed; for linear systems, the iterative process is shown to reduce to solving the familiar algebraic Ricatti equation.     

艺术设计教学中实战课题把握能力分析

课题设计的实战训练模式是高校艺术设计专业中、高年级专业课程教学中实践环节普遍操作的重要训练形式。因此在方案设计展开前,对课题的系统把握能力也就成为课题设计训练成败的先决性因素。本文正是由此进行系统探析学生在具体课题实战训练中,把握课题应着力培养与调度的综合知识能力。     

Piecewise Pseudo-Maximum Likelihood Estimation for Risk Aversion Case in First-Price Sealed-Bid Auction

In the literature, more and more clues show that the bidders indeed tend to risk averse. But as a good structure estimation procedure in first-price sealed-bid auctions, the traditional PPMLE (Piecewise Pseudo-Maximum Likelihood Estimation) approach is only applicable to the risk neutrality case. This study generalizes this approach to the risk aversion case, and obtains a simple estimation procedure for the Pareto family of private costs. In the procedure, the estimation order of distribution parameters of private costs is not important, which is different from the traditional PPMLE method. Though there is no unique estimator for θ ₁ and γ, some guidance is given for real-world applications on the basis of our Monte Carlo simulation experiments. What’s more, this procedure can be reduced further, if one is only interested in estimating the private costs. Extensive simulation experimental results indicate that our approach outperforms or matches at least the traditional one in the case of risk neutrality, and is applicable to the risk aversion case.