Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these appro...Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.展开更多
提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得...提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。展开更多
文摘Many physical processes have nonlinear behavior which can be well represented by a polynomial NARX or NARMAX model. The identification of such models has been widely explored in literature. The majority of these approaches are for the open-loop identification. However, for reasons such as safety and production restrictions, open-loop identification cannot always be done. In such cases, closed-loop identification is necessary. This paper presents a two-step approach to closed-loop identification of the polynomial NARX/NARMAX systems with variable structure control （VSC）. First, a genetic algorithm （GA） is used to maximize the similarity of VSC signal to white noise by tuning the switching function parameters. Second, the system is simulated again and its parameters are estimated by an algorithm of the least square （LS） family. Finally, simulation examples are given to show the validity of the proposed approach.
文摘提出一种基于NARX(Nonlinear Auto-Regressive Model with Exogenous Inputs)神经网络和谐波探测法的非线性系统传递函数识别方法。该方法可基于实测响应数据,采用NARX神经网络方法对结构响应模型进行训练。在此基础上采用谐波探测法得到系统响应传递函数。选取深海半潜浮式平台及系泊系统为研究对象,计算平台及其系泊系统在不同波浪工况作用下的时域耦合响应,以波高和系泊缆张力时程作为数据集,利用NARX神经网络结合谐波探测法辨识此系泊系统的响应传递函数。采用识别的传递函数预测系泊缆在不同海况下的张力响应,并与数值计算结果进行对比,证明NARX神经网络结合谐波探测法可较好地识别系泊浮体系统的非线性响应传递函数,并能够对系泊系统的张力响应进行准确预测。
