This paper presents two distinct model updating strategies for dynamical systems with local nonlinearities based on acceleration time history responses measured spatially across the vibrating structure. Both linear and nonlinear parameters are calibrated by minimizing the selected metric based on measured and predicted response using the newly proposed variant of differential search algorithm named as multi-cluster hybrid adaptive differential search (MCHADS) algorithm. The first model updating strategy involves the decoupling of linear and nonlinear characteristics of the system. In this scheme, we first establish the dynamic stiffness matrix using input and output measurements and then the underlying linear system is alone updated using it. Later, localization of the nonlinear attachment is attempted using the inverse property of the FRF of the nonlinear system and the established dynamic stiffness matrix of the underlying linear system from the previous step. Once the linear system is updated and with the already identified location(s) of nonlinear attachment(s), the nonlinear parameters are identified by formulating it as an optimization problem using the proposed MCHADS algorithm. The major advantage of the first approach is the reduction of the complex problem of nonlinear model updating to linear model updating. The second approach involves updating both linear and nonlinear parameters simultaneously using the proposed MCHADS algorithm. Investigations have been carried out by solving several numerically simulated examples and also with the experimental data of a benchmark problem to evaluate the effectiveness of the two proposed nonlinear model updating strategies. Further, investigations are also carried out to evaluate the capability of the model updating approaches towards damage identification in initially healthy nonlinear systems. Conclusions are drawn based on these investigations, highlighting the strengths and weaknesses of the two model updating approaches

本文基于在整个振动结构上空间测量的加速时程响应，提出了两种具有局部非线性的动力系统模型更新策略。线性和非线性参数均通过使用新提出的差分搜索算法变体（称为多集群混合自适应差分搜索（MCHADS）算法），根据测得的和预测的响应最小化所选指标来进行校准。第一种模型更新策略涉及系统线性和非线性特性的解耦。在该方案中，我们首先使用输入和输出测量值建立动态刚度矩阵，然后单独使用其更新基础线性系统。之后，尝试使用非线性系统FRF的逆特性和从上一步建立的基础线性系统的动态刚度矩阵来对非线性附件进行定位。一旦线性系统被更新并且具有已经识别的非线性附件的位置，则通过使用所提出的MCHADS算法将非线性公式化为优化问题来识别非线性参数。第一种方法的主要优点是将非线性模型更新的复杂问题减少为线性模型更新。第二种方法涉及使用建议的MCHADS算法同时更新线性和非线性参数。通过解决几个数值模拟示例以及基准问题的实验数据来进行研究，以评估两种提出的非线性模型更新策略的有效性。此外，还进行了研究以评估模型更新方法在最初健康的非线性系统中进行损伤识别的能力。根据这些调查得出结论，突出了两种模型更新方法的优缺点。