The nonlinear dynamic hysteretic models used in nonlinear dynamic analysis contain
generally lots of model parameters which need to be identified accurately and effectively …

The differential system characterization of hysteretic system is well known. The problem of
estimating the parameters of this system on the basis of input-output data, possibly noise …

Particle swarm optimization (PSO), which is a new robust stochastic evolutionary
computational algorithm based on the movement and intelligence of swarms, is proposed to …

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems.
The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous …

An evolutionary algorithm-based artificial neural network (ANN) to identify structural damage
and nonlinear hysteresis parameters simultaneously is presented. To avoid the 'dimensional …

Hysteresis is a nonlinear phenomenon observed in the dynamic response behavior of
numerous structural systems under high intensity cyclic or random loading, as well as in …

This study attempts to demystify a powerful neural network approach for modelling non-
linear hysteretic systems and in turn to streamline its architecture to achieve better …

An important problem in engineering is the unknown parameters estimation in nonlinear
systems. In this paper, a novel adaptive particle swarm optimization (APSO) method is …

This paper proposes a novel nonlinear dynamical system identification method based on the
sparse regression algorithm and the separable least squares method. To effectively avoid …

This paper deals with the identification of the parameters of a smoothed hysteretic model
which was proposed by Bouc and Wen with emphasis on restoring force hysteresis. The …