کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
783607 | 1465324 | 2015 | 15 صفحه PDF | دانلود رایگان |
• Improving the performance of MCMC based system identification tools.
• Lab tests on a moving mass-beam system and a seismically loaded building frame.
When Markov chain Monte Carlo (MCMC) samplers are used in problems of system parameter identification, one would face computational difficulties in dealing with large amount of measurement data and (or) low levels of measurement noise. Such exigencies are likely to occur in problems of parameter identification in dynamical systems when amount of vibratory measurement data and number of parameters to be identified could be large. In such cases, the posterior probability density function of the system parameters tends to have regions of narrow supports and a finite length MCMC chain is unlikely to cover pertinent regions. The present study proposes strategies based on modification of measurement equations and subsequent corrections, to alleviate this difficulty. This involves artificial enhancement of measurement noise, assimilation of transformed packets of measurements, and a global iteration strategy to improve the choice of prior models. Illustrative examples cover laboratory studies on a time variant dynamical system and a bending–torsion coupled, geometrically non-linear building frame under earthquake support motions.
Journal: International Journal of Non-Linear Mechanics - Volume 71, May 2015, Pages 89–103