System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Performance of nonlinear degrading structures: Identification, validation, and prediction
Computers and Structures
Simplified models of bolted joints under harmonic loading
Computers and Structures
Identification of Bouc-Wen hysteretic systems using particle swarm optimization
Computers and Structures
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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Most of the published literature concerned with the parameter estimation of the Bouc-Wen model of hysteresis via evolutionary algorithms uses a single objective function (the mean square error between the known displacements and the estimated ones) and considers the original Bouc-Wen model of hysteresis (without degradation and pinching) in the identification process. In this paper, a novel method for the identification of the parameters of the Bouc-Wen-Baber-Noori (BWBN) model of hysteresis is presented. The methodology is based on a multi-objective evolutionary optimization algorithm called NSGA-II [39]; therefore, a set of objective functions is employed instead of the traditional single objective function. The proposed methodology identifies the structural system and allows the observation of multi-modality of the BWBN model of hysteresis. The performance of the algorithm is evaluated using simulated and real data.