The stream function and the velocity potential can be easily computed by solving the Poisson equations in a unique way for the global domain. Because of the var- ious assumptions for handling the boundary conditions, ...The stream function and the velocity potential can be easily computed by solving the Poisson equations in a unique way for the global domain. Because of the var- ious assumptions for handling the boundary conditions, the solution is not unique when a limited domain is concerned. Therefore, it is very important to reduce or eliminate the effects caused by the uncertain boundary condition. In this paper, an iterative and ad- justing method based on the Endlich iteration method is presented to compute the stream function and the velocity potential in limited domains. This method does not need an explicitly specifying boundary condition when used to obtain the effective solution, and it is proved to be successful in decomposing and reconstructing the horizontal wind field with very small errors. The convergence of the method depends on the relative value for the distances of grids in two different directions and the value of the adjusting factor. It is shown that applying the method in Arakawa grids and irregular domains can obtain the accurate vorticity and divergence and accurately decompose and reconstruct the original wind field. Hence, the iterative and adjusting method is accurate and reliable.展开更多
In this work,we explore the use of an iterative Bayesian Monte Carlo(iBMC)method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library(TENDL)framework.The goal is to probe the model and parameter s...In this work,we explore the use of an iterative Bayesian Monte Carlo(iBMC)method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library(TENDL)framework.The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data.The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code.The‘best’model set with its parameter set was obtained by comparing model calculations with selected experimental data.Three experimental data types were used:(1)reaction cross sections,(2)residual production cross sections,and(3)the elastic angular distributions.To improve our fit to experimental data,we update our‘best’parameter set—the file that maximizes the likelihood function—in an iterative fashion.Convergence was determined by monitoring the evolution of the maximum likelihood estimate(MLE)values and was considered reached when the relative change in the MLE for the last two iterations was within 5%.Once the final‘best’file is identified,we infer parameter uncertainties and covariance information to this file by varying model parameters around this file.In this way,we ensured that the parameter distributions are centered on our evaluation.The proposed method was applied to the evaluation of p+^(59)Co between 1 and 100 MeV.Finally,the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2019,JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.展开更多
An iterative learning control algorithm based on error backward association and control parameter correction has been proposed for a class of linear discrete time-invariant systems with repeated operation characterist...An iterative learning control algorithm based on error backward association and control parameter correction has been proposed for a class of linear discrete time-invariant systems with repeated operation characteristics,parameter disturbance,and measurement noise taking PD type example.Firstly,the concrete form of the accelerated learning law is presented,based on the detailed description of how the control factor is obtained in the algorithm.Secondly,with the help of the vector method,the convergence of the algorithm for the strict mathematical proof,combined with the theory of spectral radius,sufficient conditions for the convergence of the algorithm is presented for parameter determination and no noise,parameter uncertainty but excluding measurement noise,parameters uncertainty and with measurement noise,and the measurement noise of four types of scenarios respectively.Finally,the theoretical results show that the convergence rate mainly depends on the size of the controlled object,the learning parameters of the control law,the correction coefficient,the association factor and the learning interval.Simulation results show that the proposed algorithm has a faster convergence rate than the traditional PD algorithm under the same conditions.展开更多
The resolution of the multistatic passive radar imaging system(MPRIS)is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity.Moreover,the inaccuracies caused by the inaccurate trac...The resolution of the multistatic passive radar imaging system(MPRIS)is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity.Moreover,the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image.To improve the performance of an MPRIS,an imaging method based on the tomographic imaging principle is presented.Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging.Furthermore,a phase correction technique is developed for compensating for phase errors in an MPRIS.Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique.The technique is nonparametric and can be used to estimate phase errors of any form.The effectiveness and convergence of the technique are confirmed by numerical simulations.展开更多
总体最小二乘估计能够同时顾及线性模型中系数矩阵A和观测向量L的误差,平差理论相对更为严密。如果系数矩阵A的部分元素没有误差,这种总体最小二乘模型为混合总体最小二乘模型。针对混合总体最小二乘(Least squares-total least squares...总体最小二乘估计能够同时顾及线性模型中系数矩阵A和观测向量L的误差,平差理论相对更为严密。如果系数矩阵A的部分元素没有误差,这种总体最小二乘模型为混合总体最小二乘模型。针对混合总体最小二乘(Least squares-total least squares,LS-TLS)解算问题,应用测量平差中的原理和方法,推导了混合总体最小二乘的迭代逼近解算公式,通过与奇异值分解法分析比较,分析了两种解算方法具有等价性,最后通过实验数据分析得出迭代算法的有效性和合理性。展开更多
针对传统蚂蚁遗传混合算法收敛速度慢的特点,提出了一种新的动态蚂蚁遗传混合算法。新算法采用最佳融合点评估策略,动态地控制遗传算法与蚂蚁算法的调用时机,并设计了相应的信息素更新方法,有效减少了算法的冗余迭代次数,提高了搜索速度...针对传统蚂蚁遗传混合算法收敛速度慢的特点,提出了一种新的动态蚂蚁遗传混合算法。新算法采用最佳融合点评估策略,动态地控制遗传算法与蚂蚁算法的调用时机,并设计了相应的信息素更新方法,有效减少了算法的冗余迭代次数,提高了搜索速度;同时引入迭代调整阈值控制算法后期的遗传操作和蚂蚁规模,加快了种群进化速度,从而更快地找到最优解。通过对Muth and Thompson基准问题进行计算机仿真,实验证明新算法收敛速度得到了提高。展开更多
基金Project supported by the National Natural Science Foundation of China (No.40975031)
文摘The stream function and the velocity potential can be easily computed by solving the Poisson equations in a unique way for the global domain. Because of the var- ious assumptions for handling the boundary conditions, the solution is not unique when a limited domain is concerned. Therefore, it is very important to reduce or eliminate the effects caused by the uncertain boundary condition. In this paper, an iterative and ad- justing method based on the Endlich iteration method is presented to compute the stream function and the velocity potential in limited domains. This method does not need an explicitly specifying boundary condition when used to obtain the effective solution, and it is proved to be successful in decomposing and reconstructing the horizontal wind field with very small errors. The convergence of the method depends on the relative value for the distances of grids in two different directions and the value of the adjusting factor. It is shown that applying the method in Arakawa grids and irregular domains can obtain the accurate vorticity and divergence and accurately decompose and reconstruct the original wind field. Hence, the iterative and adjusting method is accurate and reliable.
基金Funding Open Access funding provided by Lib4RI–Library for the Research Institutes within the ETH Domain:Eawag,Empa,PSI&WSLthe Paul Scherrer Institute through the NES/GFA-ABE Cross Project.
文摘In this work,we explore the use of an iterative Bayesian Monte Carlo(iBMC)method for nuclear data evaluation within a TALYS Evaluated Nuclear Data Library(TENDL)framework.The goal is to probe the model and parameter space of the TALYS code system to find the optimal model and parameter sets that reproduces selected experimental data.The method involves the simultaneous variation of many nuclear reaction models as well as their parameters included in the TALYS code.The‘best’model set with its parameter set was obtained by comparing model calculations with selected experimental data.Three experimental data types were used:(1)reaction cross sections,(2)residual production cross sections,and(3)the elastic angular distributions.To improve our fit to experimental data,we update our‘best’parameter set—the file that maximizes the likelihood function—in an iterative fashion.Convergence was determined by monitoring the evolution of the maximum likelihood estimate(MLE)values and was considered reached when the relative change in the MLE for the last two iterations was within 5%.Once the final‘best’file is identified,we infer parameter uncertainties and covariance information to this file by varying model parameters around this file.In this way,we ensured that the parameter distributions are centered on our evaluation.The proposed method was applied to the evaluation of p+^(59)Co between 1 and 100 MeV.Finally,the adjusted files were compared with experimental data from the EXFOR database as well as with evaluations from the TENDL-2019,JENDL/He-2007 and JENDL-4.0/HE nuclear data libraries.
文摘An iterative learning control algorithm based on error backward association and control parameter correction has been proposed for a class of linear discrete time-invariant systems with repeated operation characteristics,parameter disturbance,and measurement noise taking PD type example.Firstly,the concrete form of the accelerated learning law is presented,based on the detailed description of how the control factor is obtained in the algorithm.Secondly,with the help of the vector method,the convergence of the algorithm for the strict mathematical proof,combined with the theory of spectral radius,sufficient conditions for the convergence of the algorithm is presented for parameter determination and no noise,parameter uncertainty but excluding measurement noise,parameters uncertainty and with measurement noise,and the measurement noise of four types of scenarios respectively.Finally,the theoretical results show that the convergence rate mainly depends on the size of the controlled object,the learning parameters of the control law,the correction coefficient,the association factor and the learning interval.Simulation results show that the proposed algorithm has a faster convergence rate than the traditional PD algorithm under the same conditions.
基金Project supported by the National Natural Science Foundation of China(No.61401526)the Innovative Research Team in University,China(No.IRT0954)the Foundation of National Ministries,China(No.9140A07020614DZ01)
文摘The resolution of the multistatic passive radar imaging system(MPRIS)is poor due to the narrow bandwidth of the signal transmitted by illuminators of opportunity.Moreover,the inaccuracies caused by the inaccurate tracking system or the error position measurement of illuminators or receivers can deteriorate the quality of an image.To improve the performance of an MPRIS,an imaging method based on the tomographic imaging principle is presented.Then the compressed sensing technique is extended to the MPRIS to realize high-resolution imaging.Furthermore,a phase correction technique is developed for compensating for phase errors in an MPRIS.Phase errors can be estimated by iteratively solving an equation that is derived by minimizing the mean recovery error of the reconstructed image based on the principle of fixed-point iteration technique.The technique is nonparametric and can be used to estimate phase errors of any form.The effectiveness and convergence of the technique are confirmed by numerical simulations.
文摘总体最小二乘估计能够同时顾及线性模型中系数矩阵A和观测向量L的误差,平差理论相对更为严密。如果系数矩阵A的部分元素没有误差,这种总体最小二乘模型为混合总体最小二乘模型。针对混合总体最小二乘(Least squares-total least squares,LS-TLS)解算问题,应用测量平差中的原理和方法,推导了混合总体最小二乘的迭代逼近解算公式,通过与奇异值分解法分析比较,分析了两种解算方法具有等价性,最后通过实验数据分析得出迭代算法的有效性和合理性。
文摘针对传统蚂蚁遗传混合算法收敛速度慢的特点,提出了一种新的动态蚂蚁遗传混合算法。新算法采用最佳融合点评估策略,动态地控制遗传算法与蚂蚁算法的调用时机,并设计了相应的信息素更新方法,有效减少了算法的冗余迭代次数,提高了搜索速度;同时引入迭代调整阈值控制算法后期的遗传操作和蚂蚁规模,加快了种群进化速度,从而更快地找到最优解。通过对Muth and Thompson基准问题进行计算机仿真,实验证明新算法收敛速度得到了提高。
