基于稀疏 Bayes 学习算法的无约束结构 荷载重构方法.

For rapid and exact reconstruction of dynamic loads on unconstrained structures with unknown initial conditions, a dynamic load reconstruction method was proposed based on the sparse Bayesian learning algorithm. With the idea of the function fitting technique, the control equations were built. The noise was assumed to obey the Gaussian distribution, and the fast algorithm was used in the sparse Bayesian learning model. An improved piecewise fitting method was formulated to rationally express the initial conditions in the piece- wise fitting, the end state response of the previous segment was used as the possible initial condition, and thelow-order vibration modes were applied as the supplement to the initial displacements and initial velocities. The numerical simulations of simplified launch vehicle models prove the accuracy and efficiency of the proposed method, under the effects of different noise levels and different expressions of initial conditions. [ABSTRACT FROM AUTHOR]