A widely used model in the field of hysteretic or memory-dependent vibratio
ns is that of Bone and Wen. Different parameter values extend its use to va
rious areas of mechanical vibrations. As a consequence an identification me
thod is required to identify the parameter values relevant to its applicati
on. Its structure, however, includes internal states and non-linear terms.
This rules out the conventional identification methods, such as least squar
es and maximum likelihood because they require derivative calculations of t
he prediction error with respect to the parameters. In this paper are prese
nted some results for Bouc-Wen model identification, using simulated noise-
free data, simulated noisy data and experimental data obtained from a nucle
ar power plant. The method used to achieve this is the differential evoluti
on algorithm. Differential evolution (DE) is an optimization method develop
ed to perform direct search in a continuous parameter space without requiri
ng any derivative estimation. (C) 2001 Academic Press.