针对体系优化存在的体系建模困难、难以量化反映体系效能问题,本文在深入分析武器装备体系结构基础上,通过基于Agent的建模和图示评审技术(graphical evaluation and review technique,GERT)构建具有自学习机制的体系A-GERT网络,实现体...针对体系优化存在的体系建模困难、难以量化反映体系效能问题,本文在深入分析武器装备体系结构基础上,通过基于Agent的建模和图示评审技术(graphical evaluation and review technique,GERT)构建具有自学习机制的体系A-GERT网络,实现体系效能优化。其次,基于矩母函数与梅森公式给出了体系作战链/网任务成功概率和作战效能的计算方法和证明,并在深刻剖析体系组成单元贡献的基础上,借助合作博弈的利益公平分配思想,提出了基于Shapley值的体系组成单元期望贡献评估模型。然后,基于马尔可夫过程理论,提出了基于组成单元贡献的A-GERT网络体系效能优化算法。最后结合实例研究,说明了所提模型的可行性和有效性。展开更多
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
文摘针对体系优化存在的体系建模困难、难以量化反映体系效能问题,本文在深入分析武器装备体系结构基础上,通过基于Agent的建模和图示评审技术(graphical evaluation and review technique,GERT)构建具有自学习机制的体系A-GERT网络,实现体系效能优化。其次,基于矩母函数与梅森公式给出了体系作战链/网任务成功概率和作战效能的计算方法和证明,并在深刻剖析体系组成单元贡献的基础上,借助合作博弈的利益公平分配思想,提出了基于Shapley值的体系组成单元期望贡献评估模型。然后,基于马尔可夫过程理论,提出了基于组成单元贡献的A-GERT网络体系效能优化算法。最后结合实例研究,说明了所提模型的可行性和有效性。
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
