The precise integration method proposed for linear time-invariant homogeneous dynamic systems can provide accurate numerical results that approach an exact solution at integration points. However, difficulties arise w...The precise integration method proposed for linear time-invariant homogeneous dynamic systems can provide accurate numerical results that approach an exact solution at integration points. However, difficulties arise when the algorithm is used for non-homogeneous dynamic systems due to the inverse matrix calculation required. In this paper, the structural dynamic equalibrium equations are converted into a special form, the inverse matrix calculation is replaced by the Crout decomposition method to solve the dynamic equilibrium equations, and the precise integration method without the inverse matrix calculation is obtained. The new algorithm enhances the present precise integration method by improving both the computational accuracy and efficiency. Two numerical examples are given to demonstrate the validity and efficiency of the proposed algorithm.展开更多
In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are define...In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are defined for our DPV.The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition.An appropriate population variation formula is suggested to accelerate convergence.The efficacy of these innovations in our DPV is examined using six benchmark problems.Comparison among the difierent characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures.It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases.Meanwhile,our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.展开更多
文摘The precise integration method proposed for linear time-invariant homogeneous dynamic systems can provide accurate numerical results that approach an exact solution at integration points. However, difficulties arise when the algorithm is used for non-homogeneous dynamic systems due to the inverse matrix calculation required. In this paper, the structural dynamic equalibrium equations are converted into a special form, the inverse matrix calculation is replaced by the Crout decomposition method to solve the dynamic equilibrium equations, and the precise integration method without the inverse matrix calculation is obtained. The new algorithm enhances the present precise integration method by improving both the computational accuracy and efficiency. Two numerical examples are given to demonstrate the validity and efficiency of the proposed algorithm.
基金Ministry of Major Science & Technology of Shanghai(No.10DZ1200204)
文摘In this paper,we propose genetic programming(GP) using dynamic population variation(DPV) with four innovations for reducing computational efforts.A new stagnation phase definition and characteristic measure are defined for our DPV.The exponential pivot function is proposed to our DPV method in conjunction with the new stagnation phase definition.An appropriate population variation formula is suggested to accelerate convergence.The efficacy of these innovations in our DPV is examined using six benchmark problems.Comparison among the difierent characteristic measures has been conducted for regression problems and the new proposed measure outperformed other measures.It is proved that our DPV has the capacity to provide solutions at a lower computational effort compared with previously proposed DPV methods and standard genetic programming in most cases.Meanwhile,our DPV approach introduced in GP could also rapidly find an excellent solution as well as standard GP in system modeling problems.
