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 …

The development of suitable mathematical models on the basis of dynamic measurements
from dispersed structural systems that may be undergoing significant nonlinear behavior is …

Hysteretic models are used to describe the nonlinear memory-based relationship between
the input and output of some physical systems. A long short-term memory neural network …

This paper addresses the modeling problem of nonlinear and hysteretic dynamic behaviors
through a constructive modeling approach which exploits existing mathematical concepts in …

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

The experimental dynamical response of three types of nonlinear hysteretic systems is
identified employing phenomenological models togheter with the Differential Evolutionary …

Nonlinear hysteretic systems are common in many engineering problems. The maximum
response estimation of a nonlinear hysteretic system under stochastic excitations is an …

An identification method is developed for nonlinear hysteretic systems by use of artificial
neural network in the paper. Employing the Bouc–Wen differential model widely used for …

This paper introduces explicit neural representations of fundamental hysteresis operators
such as the play and stop operators. The hysteresis neurons are represented by recurrent …

Cyclic behavior of materials is complex and difficult to model. A combination of hardening
rules in classical plasticity is one possibility for modeling this complex material behavior …