In this article, we obtain explicit solutions of a linear PDE subject to a class of ra-dial square integrable functions with a monotonically increasing weight function｜x｜n-1eβ｜x｜2/2,β ≥ 0, x ∈ Rn. This linear ...In this article, we obtain explicit solutions of a linear PDE subject to a class of ra-dial square integrable functions with a monotonically increasing weight function｜x｜n-1eβ｜x｜2/2,β ≥ 0, x ∈ Rn. This linear PDE is obtained from a system of forced Burgers equation via the Cole-Hopf transformation. For any spatial dimension n＆gt;1, the solution is expressed in terms of a family of weighted generalized Laguerre polynomials. We also discuss the large time behaviour of the solution of the system of forced Burgers equation.展开更多
According to the issues that the predict errors of chaotic sequences rapidly accumulated in multi-step forecasting which affects the predict accuracy, we proposed a new predict algorithm based on local modeling with v...According to the issues that the predict errors of chaotic sequences rapidly accumulated in multi-step forecasting which affects the predict accuracy, we proposed a new predict algorithm based on local modeling with variable frame length and interpolation points. The core idea is that, using interpolation method to increase the available sample data, then modeling the chaos dynamics system with least square algorithm which based on the Bernstein polynomial to realize the forecasting. We use the local modeling method, looking for the optimum frame length and interpolation points in every frame to improve the predict peformance. The experimental results show that the proposed algorithm can improve the predictive ability effectively, decreasing the accumulation of iterative errors in multi-step prediction.展开更多
针对杂波环境下的目标跟踪问题,提出了一种基于变分贝叶斯的概率数据关联算法(Variational Bayesian based probabilistic data association algorithm, VB-PDA).该算法首先将关联事件视为一个随机变量并利用多项分布对其进行建模,随后...针对杂波环境下的目标跟踪问题,提出了一种基于变分贝叶斯的概率数据关联算法(Variational Bayesian based probabilistic data association algorithm, VB-PDA).该算法首先将关联事件视为一个随机变量并利用多项分布对其进行建模,随后基于数据集、目标状态、关联事件的联合概率密度函数求取关联事件的后验概率密度函数,最后将关联事件的后验概率密度函数引入变分贝叶斯框架中以获取状态近似后验概率密度函数.相比于概率数据关联算法, VB-PDA算法在提高算法实时性的同时在权重Kullback-Leibler (KL)平均准则下获取了近似程度更高的状态后验概率密度函数.相关仿真实验对提出算法的有效性进行了验证.展开更多
基金supported by Research Grants of National Board for Higher Mathematics(Award No:2/40(13)/2010-R&D-II/8911)UGC’s Dr.D.S.Kothari Fellowship(Award No.F.4-2/2006(BSR)/13-440/2011(BSR))
文摘In this article, we obtain explicit solutions of a linear PDE subject to a class of ra-dial square integrable functions with a monotonically increasing weight function｜x｜n-1eβ｜x｜2/2,β ≥ 0, x ∈ Rn. This linear PDE is obtained from a system of forced Burgers equation via the Cole-Hopf transformation. For any spatial dimension n＆gt;1, the solution is expressed in terms of a family of weighted generalized Laguerre polynomials. We also discuss the large time behaviour of the solution of the system of forced Burgers equation.
基金Supported by the Research Project of China Scholarship Council(No.201208155076)the Natural Science Foundation of Inner Mongolia(No.2013MS0118)the College Science Research Project of Inner Mongolia(No.NJZZ12182,No.NJZY13268)~~
文摘According to the issues that the predict errors of chaotic sequences rapidly accumulated in multi-step forecasting which affects the predict accuracy, we proposed a new predict algorithm based on local modeling with variable frame length and interpolation points. The core idea is that, using interpolation method to increase the available sample data, then modeling the chaos dynamics system with least square algorithm which based on the Bernstein polynomial to realize the forecasting. We use the local modeling method, looking for the optimum frame length and interpolation points in every frame to improve the predict peformance. The experimental results show that the proposed algorithm can improve the predictive ability effectively, decreasing the accumulation of iterative errors in multi-step prediction.
文摘针对杂波环境下的目标跟踪问题,提出了一种基于变分贝叶斯的概率数据关联算法(Variational Bayesian based probabilistic data association algorithm, VB-PDA).该算法首先将关联事件视为一个随机变量并利用多项分布对其进行建模,随后基于数据集、目标状态、关联事件的联合概率密度函数求取关联事件的后验概率密度函数,最后将关联事件的后验概率密度函数引入变分贝叶斯框架中以获取状态近似后验概率密度函数.相比于概率数据关联算法, VB-PDA算法在提高算法实时性的同时在权重Kullback-Leibler (KL)平均准则下获取了近似程度更高的状态后验概率密度函数.相关仿真实验对提出算法的有效性进行了验证.
