POSITIVE SOLUTIONS OF SINGULAR SUBLINEAR SECOND ORDER BOUNDARY VALUE PROBLEMS

1.IntroductionBoundaryvaluepr0blemsconcerningthegeneralizedEmden-F0wlerequationsx p(t)x'=O,(1)wherep(t)eC(O,1),p(t)20f0rtE(O,1),AER,havebeenstudiedbymanyauthors(see[1-51andthereferencestherein).InthispaperwetreattheDirichletproblem(1)withax(0)-bx'(0)=O,c…     

不完全信息下基于证据推理的竞争对手分析

针对不完全信息下竞争对手分析问题,本文提出一种基于证据推理的分析方法。该方法首先定义了正负相似度对竞争者进行比较,接着通过证据推理对含有未知信息的相似度进行合成排序,最后根据信息熵原理度量结论的客观可靠度。与本文提及的其他方法相比,该方法可以在信息不完全的情况下识别分析竞争对手并提供关于结论的客观可靠度,符合商业竞争的实际特点,具有较强的实用价值。文中的案例分析证实了该方法的有效性。     

群决策的交互式TOPSIS方法及其应用

建立了一种多轮交互逐步逼近满意解的多属性群体决策的综合方法,对决策方案采用扩展的 TOPSIS(technique for order preference by similarity to ideal solution)法进行定性评价排序,以克服一类难以建立解析模型,分析求解困难的多属性群决策问题;设计了群体满意度等软指标来分析计算,判断群体一致性的达成,最终得出群体满意的方案排序.通过应用实例,说明了该方法的有效性和可行性,初步提出了可作为建立一类实际的群决策支持系统的方法和技术框架.     

一类径向基神经网络干扰观测器轨迹线性化控制

利用径向基神经网络(RBFNN)的逼近能力,研究了基于径向基神经网络干扰观测器(RDO)的鲁棒自适应轨迹线性化控制(TLC)策略,以解决空天飞行器复杂飞行条件下系统不确定及干扰的控制问题。分析了系统存在不确定性时轨迹线性化控制方法性能降低甚至失效的原因,设计了自适应调节律,并采用Lyapunov方法严格证明了在该自适应调节律作用下闭环系统所有误差信号最终有界。仿真结果表明,较当前TLC方法的控制性能,新方案在空天飞行器系统上具有更优异的控制性能和鲁棒性。     

地面站数传系统的RBFNN模型及算法

针对难以建立精确数学模型的地面站数传系统,提出改进梯度迭代学习的径向基神经网络建模方法。改进梯度学习算法通过训练样本相关性矩阵的主成分分析确定网络隐含层初始节点数;改进迭代过程中网络参数的梯度信息计算方式,加快了迭代收敛速度;并增加结构调整过程,实现对网络规模的精简。通过采集地面站数传系统输入-输出数据,将改进梯度学习算法应用于网络离线训练,并给出具体实现步骤。地面站数传资源配置优化实例验证了模型具有较高泛化能力,且算法稳定性较佳。     

The M/PH/1 queue with working vacations and vacation interruption

We study an M/PH/1 queue with phase type working vacation and vacation interruption where the vacation time follows a phase type distribution. The server serves the customers at a lower rate in a vacation period. The server comes back to the regular busy period at a service completion without completing the vacation. Such policy is called vacation interruption. In terms of quasi birth and death process and matrix-geometric solution method, we obtain the stationary queue length distribution. Moreover we obtain the conditional stochastic decomposition structures of queue length and waiting time when the service time distribution in the regular busy period is exponential.     

基于岭回归径向基神经网络的MIMU误差建模

对MEMS（Micro Electro Mechanical systems微电子机械系统）IMU（Inertial Measurement Unit惯性测量单元）的随机漂移和误差进行建模和补偿是提高捷联惯导系统性能的主要方法之一。为了避免径向基（RBF）神经网络输出之间可能存在的复共线性,更加准确有效的对MIMU的输出误差建模,提出将岭回归径向基神经网络用于MIMU的误差建模中。通过与时间序列建模方法的仿真比较表明,在对MIMU误差补偿上所建立的岭回归RBF网络与四阶AR模型方法精度相当,比一阶AR模型精度高,而且无需对数据平稳性处理。

Abstract：

It is one of the main methods to improve the performance of Strap-down Inertial Navigation System for compensating the random drift and bias of MEMS （Micro Electro Mechanical systems） IMU （Inertial Measurement Unit）. In order to eliminate latent multicollinearity of radial basis function neuron network output layer and model the drift and bias of MIMU accurately,the radial basis function neuron network based on ridge regression method was proposed which was applied in modeling and compensating MIMU errors. The simulation shows,compared to the AR model,the precision of compensation of MIMU error using radial basis function neuron network based on ridge regression method is equal to the fourth order AR model,better than first order AR model,and no data stabilization processing.     

基于航迹关联的STLFMCW雷达多目标配对

首先分析了STLFMCW雷达估计目标径向运动速度的原理。针对利用频谱特征进行目标配对求取速度方法存在的缺陷,将多目标跟踪技术应用于目标配对,分别对上、下扫频段多目标回波起始"局部航迹",在"局部航迹"正确起始后,利用同一目标上、下扫频段内"局部航迹"的相关性实现了多目标配对;并通过配对确认矩阵的拆分减小了配对的多义性,从而显著提高了目标正确配对的概率。仿真结果表明,在复杂多目标与杂波环境下,该方法能有效实现上、下扫频段目标回波的正确配对。     

Preface

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer’s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties. This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 60674082, 70221001, and 70731003.     

Tax Evasion: Models with Self-Audit

Different from traditional tax audit, supervision with self-audit is a combination of audit by the taxpayer himself and audit by the tax authority. This paper mainly studies a taxpayer＇s optimal policy of tax evasion under supervision with self-audit and its related properties, in order to deduce some effective suggestions and theoretical bases to restrain tax evasion. Assuming that only a certain proportion of evaded tax can be discovered when the audit is executed, the authors first formulate a static model with self-audit. This model is divided into two stages. At the first stage, taxpayers declare their taxes, then the tax authority chooses audit objects, based on a known probability, and announces the result; if the taxpayer is chosen, he will enter the second stage, during which he has a chance to pay the evaded tax and the corresponding late fees and then is audited by the tax authority. The authors show the existence and uniqueness of the optimal amount of tax evasion at the first stage and the optimal proportion to self-expose at the second stage. The authors also discuss the related properties of the interior solution, and do elasticity analyses on some parameters. Besides, the authors extend the static model into the corresponding two-period model, and study the existence and uniqueness of the solution of the extended model. Finally, under the assumption that tax evasion can only be discovered with a certain probability when the audit is executed, the authors formulate another static model with self-audit and investigate its properties.