针对基于L_1自适应控制的超低空空投纵向控制器,利用基于参数空间寻优(parameter space investigation,PSI)的多准则优化方法,以空投任务性能等级为指标,对L_1自适应控制器中状态估计器短周期阻尼比、自然频率以及低通滤波器带宽这3个...针对基于L_1自适应控制的超低空空投纵向控制器,利用基于参数空间寻优(parameter space investigation,PSI)的多准则优化方法,以空投任务性能等级为指标,对L_1自适应控制器中状态估计器短周期阻尼比、自然频率以及低通滤波器带宽这3个参数进行优化,通过两次迭代,最终求得合适的Pareto最优解以完成参数优化的过程.仿真验证了参数寻优过程的有效性和适用性,证明了经参数优化后,超低空空投任务性能等级由"适度"改善至"期望", L_1控制器的动态性能和鲁棒性能提升显著,可有效保证运输机的安全性和空投任务的顺利完成.展开更多
The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theore...The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective.展开更多
In this note, the exact value of the James constant for the l3 - l1 space is obtained, J（l3 - l0 = 1.5573.... This result improves the known inequality, J（13 - 11） ≤4/3√10,which was given by Dhompongsa, Piraisang...In this note, the exact value of the James constant for the l3 - l1 space is obtained, J（l3 - l0 = 1.5573.... This result improves the known inequality, J（13 - 11） ≤4/3√10,which was given by Dhompongsa, Piraisangjun and Saejung.展开更多
The goal of this paper is to achieve a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is...The goal of this paper is to achieve a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is to utilize the principal shift invariant(PSI) space and the l_1 norm minimization. In order to obtain different sparsity of the approximation solution, the problem is represented as a multilevel LASSO(MLASSO)model with different regularization parameters. The MLASSO model can be solved efficiently by the alternating direction method of multipliers. Numerical experiments indicate that compared to the AGLASSO model and the basic MBA algorithm, the MLASSO model can provide an acceptable compromise between the minimization of the data mismatch term and the sparsity of the solution. Moreover, the solution by the MLASSO model can reflect the regions of the underlying surface where high gradients occur.展开更多
文摘针对基于L_1自适应控制的超低空空投纵向控制器,利用基于参数空间寻优(parameter space investigation,PSI)的多准则优化方法,以空投任务性能等级为指标,对L_1自适应控制器中状态估计器短周期阻尼比、自然频率以及低通滤波器带宽这3个参数进行优化,通过两次迭代,最终求得合适的Pareto最优解以完成参数优化的过程.仿真验证了参数寻优过程的有效性和适用性,证明了经参数优化后,超低空空投任务性能等级由"适度"改善至"期望", L_1控制器的动态性能和鲁棒性能提升显著,可有效保证运输机的安全性和空投任务的顺利完成.
文摘The maximum of k numerical functions defined on , , by , ??is used here in Statistical classification. Previously, it has been used in Statistical Discrimination [1] and in Clustering [2]. We present first some theoretical results on this function, and then its application in classification using a computer program we have developed. This approach leads to clear decisions, even in cases where the extension to several classes of Fisher’s linear discriminant function fails to be effective.
基金Supported by National Natural Science Foundation of China(Grant Nos.11271112,11201127)IRTSTHN(Grant No.14IRTSTHN023)
文摘In this note, the exact value of the James constant for the l3 - l1 space is obtained, J（l3 - l0 = 1.5573.... This result improves the known inequality, J（13 - 11） ≤4/3√10,which was given by Dhompongsa, Piraisangjun and Saejung.
基金The research was supported by the National Natural Science Foundation of China #10625105 and #10431060, the Program for New Century Excellent Talents in University #NCET-04-0745. Acknowledgement Authors would like to thank the anonymous referee for his/her helpful suggestions and comments.
基金supported by National Natural Science Foundation of China(Grant Nos.11526098,11001037,11290143 and 11471066)the Research Foundation for Advanced Talents of Jiangsu University(Grant No.14JDG034)+1 种基金the Natural Science Foundation of Jiangsu Province(Grant No.BK20160487)the Fundamental Research Funds for the Central Universities(Grant No.DUT15LK44)
文摘The goal of this paper is to achieve a computational model and corresponding efficient algorithm for obtaining a sparse representation of the fitting surface to the given scattered data. The basic idea of the model is to utilize the principal shift invariant(PSI) space and the l_1 norm minimization. In order to obtain different sparsity of the approximation solution, the problem is represented as a multilevel LASSO(MLASSO)model with different regularization parameters. The MLASSO model can be solved efficiently by the alternating direction method of multipliers. Numerical experiments indicate that compared to the AGLASSO model and the basic MBA algorithm, the MLASSO model can provide an acceptable compromise between the minimization of the data mismatch term and the sparsity of the solution. Moreover, the solution by the MLASSO model can reflect the regions of the underlying surface where high gradients occur.
