Building collapses during recent earthquakes have brought up the need for research on factors pertaining to collapse and the safety of structures.This requires response replication of structures that account for uncer...Building collapses during recent earthquakes have brought up the need for research on factors pertaining to collapse and the safety of structures.This requires response replication of structures that account for uncertainties from ground motions and structural properties.Structural collapse often implies that the structural system is no longer capable of maintaining its gravity load-carrying capacity,which often points to factors involving strength and stiffness degradation.In this study,the polynomial chaos nonlinear autoregressive with exogenous input form(PC-NARX)model is explored for dynamic response replication of a nonlinear single-degree-of-freedom(SDOF)structure.The generalized hysteretic Bouc-Wen model is applied to emulate stiffness and strength degradation for an SDOF structure close to collapse.A stochastic ground motion model is used to represent the uncertainties in seismic excitation.The PC-NARX model is employed and further evaluated for response replication of an SDOF system with inherent uncertain structural properties.A generic algorithm(GA)is used to select the terms for structural dynamics,and polynomial chaos expansion(PCE)is used to incorporate uncertain parameters into NARX model coefficients.It is demonstrated that the PC-NARX model provides good accuracy to account for both ground motion and structural uncertainties into response replication of SDOF structures with significant strength and stiffness degradation.The PC-NARX model thus presents a promising technique for collapse safety analysis of structures.展开更多
非线性有源自回归(Nonlinear Auto Regressive eXogenous,NARX)模型在辨识过程中要求以随机激励作为输入信号,由于转子系统无法产生该信号,因此传统辨识方法不适用于此类系统。针对该问题,提出一种频域模型结构及对应的扫频辨识方法。首...非线性有源自回归(Nonlinear Auto Regressive eXogenous,NARX)模型在辨识过程中要求以随机激励作为输入信号,由于转子系统无法产生该信号,因此传统辨识方法不适用于此类系统。针对该问题,提出一种频域模型结构及对应的扫频辨识方法。首先,通过快速傅里叶变换(Fast Fourier Transform,FFT)得到系统扫频过程中全部时域响应的频谱,提取关键频率成分对应的频域数据,并组建频域模型结构下的候选模型项库。其次,引入基于预测残差平方和的正交向前回归算法(the Orthogonal Forward Regression algorithm,based on the Predictive Residual Sum of Squares,PRESS-based OFR)完成模型项选取。最后,通过简单转子系统数学模型得到的仿真数据及转子实验台得到的真实数据验证所提出建模方法的有效性和适用性,同时,也通过上述两种数据探讨了扫频过程中引入不同的频率成分信息对预测精度的影响。结果表明,采用频域数据得到的模型结构和对应的系数能准确预测系统时域响应。在仿真数据上,模型预测的归一化均方误差(Normalized Mean Square Error,NMSE)不超过0.3%,而实验数据的NMSE进一步降低至0.2%。此外,通过对比不同模型结构,包含二倍频对应数据的模型(模型二)在预测系统二倍频处对应的幅值时的精度比仅使用基频数据建立的模型(模型一)更准确,NMSE在转速为339.3 rad/s工况下提升了95.58%;因此,丰富的频域数据信息有利于得到更准确的模型,提出的方法为谐波激励系统的辨识提供了新思路。展开更多
基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经...基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经网络、长短期记忆网络(long short term memory,LSTM)神经网络及量产发动机的排温传感器采集结果进行对比。经验证,稳态工况下,两种神经网络均能达到较高精度;欧洲瞬态循环(European transient cycle,ETC)工况下,NARX神经网络计算温度的最大偏差为6.6℃,量产发动机排温传感器测得温度最大偏差为45.9℃。NARX神经网络所需的计算时间约为现有电控单元排温模型的2.5倍。展开更多
针对非线性自回归模型(Nonlinear Auto-Regressive with extrainput,NARX)系统辨识问题,利用非正交的方法来构造较为稀疏的逼近NARX模型的径向基函数模型。与已有的径向基或其他的核模型只采用同一固定的尺度不同,采用多个尺度,通过最...针对非线性自回归模型(Nonlinear Auto-Regressive with extrainput,NARX)系统辨识问题,利用非正交的方法来构造较为稀疏的逼近NARX模型的径向基函数模型。与已有的径向基或其他的核模型只采用同一固定的尺度不同,采用多个尺度,通过最小化当前训练误差,选择最佳的核中心和尺度参数。在学习过程中,采用非正交核函数的方法进行模型逐步回归。对样本数据利用k均值聚类算法得到核函数中心参数备选项,同时设置多个备选尺度,并通过最小二乘法求得相应核函数的权值,利用前向选择方法从中找出使模型误差最小的最优核函数。仿真实验验证了方法在泛化性能和稀疏性方面的可行性。展开更多
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
针对SI(Spark Ignition)发动机空燃比(AFR:Air-Fuel Ratio)控制精度低、无法自适应等问题,提出了基于NARX(Nonlinear Auto Regressive model with e Xogenous inputs)模型的非线性模型预测控制(NMPC:Nonlinear Model Predict Control)...针对SI(Spark Ignition)发动机空燃比(AFR:Air-Fuel Ratio)控制精度低、无法自适应等问题,提出了基于NARX(Nonlinear Auto Regressive model with e Xogenous inputs)模型的非线性模型预测控制(NMPC:Nonlinear Model Predict Control)方法。利用渐消记忆递推最小二乘(RLS:Recursive Least Squares)算法对NARX模型进行辨识,基于NARX模型对SI发动机的AFR进行非线性模型预测控制。该方法辨识精度高,可通过NARX模型数学结构直接计算最优控制序列,从而提高系统的控制精度。同时,采用Matlab对均值发动机模型(MVEM:Mean Value Engine Model)进行仿真实验,并与采用Volterra模型的PI(Proportional Integral)控制器算法进行对比。仿真结果证明,该算法控制效果比基于Volterra模型和传统的PI控制器的控制效果超调量小,调节时间短,更加具有工程实际应用性。展开更多
基金National Natural Science Foundation of China under Grant No.51878390。
文摘Building collapses during recent earthquakes have brought up the need for research on factors pertaining to collapse and the safety of structures.This requires response replication of structures that account for uncertainties from ground motions and structural properties.Structural collapse often implies that the structural system is no longer capable of maintaining its gravity load-carrying capacity,which often points to factors involving strength and stiffness degradation.In this study,the polynomial chaos nonlinear autoregressive with exogenous input form(PC-NARX)model is explored for dynamic response replication of a nonlinear single-degree-of-freedom(SDOF)structure.The generalized hysteretic Bouc-Wen model is applied to emulate stiffness and strength degradation for an SDOF structure close to collapse.A stochastic ground motion model is used to represent the uncertainties in seismic excitation.The PC-NARX model is employed and further evaluated for response replication of an SDOF system with inherent uncertain structural properties.A generic algorithm(GA)is used to select the terms for structural dynamics,and polynomial chaos expansion(PCE)is used to incorporate uncertain parameters into NARX model coefficients.It is demonstrated that the PC-NARX model provides good accuracy to account for both ground motion and structural uncertainties into response replication of SDOF structures with significant strength and stiffness degradation.The PC-NARX model thus presents a promising technique for collapse safety analysis of structures.
文摘非线性有源自回归(Nonlinear Auto Regressive eXogenous,NARX)模型在辨识过程中要求以随机激励作为输入信号,由于转子系统无法产生该信号,因此传统辨识方法不适用于此类系统。针对该问题,提出一种频域模型结构及对应的扫频辨识方法。首先,通过快速傅里叶变换(Fast Fourier Transform,FFT)得到系统扫频过程中全部时域响应的频谱,提取关键频率成分对应的频域数据,并组建频域模型结构下的候选模型项库。其次,引入基于预测残差平方和的正交向前回归算法(the Orthogonal Forward Regression algorithm,based on the Predictive Residual Sum of Squares,PRESS-based OFR)完成模型项选取。最后,通过简单转子系统数学模型得到的仿真数据及转子实验台得到的真实数据验证所提出建模方法的有效性和适用性,同时,也通过上述两种数据探讨了扫频过程中引入不同的频率成分信息对预测精度的影响。结果表明,采用频域数据得到的模型结构和对应的系数能准确预测系统时域响应。在仿真数据上,模型预测的归一化均方误差(Normalized Mean Square Error,NMSE)不超过0.3%,而实验数据的NMSE进一步降低至0.2%。此外,通过对比不同模型结构,包含二倍频对应数据的模型(模型二)在预测系统二倍频处对应的幅值时的精度比仅使用基频数据建立的模型(模型一)更准确,NMSE在转速为339.3 rad/s工况下提升了95.58%;因此,丰富的频域数据信息有利于得到更准确的模型,提出的方法为谐波激励系统的辨识提供了新思路。
文摘基于台架采集数据,采用外部输入非线性自回归(nonlinear autoregressive model with exogenous input,NARX)神经网络建立了具备瞬态特性的柴油机排气温度计算模型作为虚拟传感器,并采用并发式训练方法对模型进行训练。将结果与前馈神经网络、长短期记忆网络(long short term memory,LSTM)神经网络及量产发动机的排温传感器采集结果进行对比。经验证,稳态工况下,两种神经网络均能达到较高精度;欧洲瞬态循环(European transient cycle,ETC)工况下,NARX神经网络计算温度的最大偏差为6.6℃,量产发动机排温传感器测得温度最大偏差为45.9℃。NARX神经网络所需的计算时间约为现有电控单元排温模型的2.5倍。
文摘针对非线性自回归模型(Nonlinear Auto-Regressive with extrainput,NARX)系统辨识问题,利用非正交的方法来构造较为稀疏的逼近NARX模型的径向基函数模型。与已有的径向基或其他的核模型只采用同一固定的尺度不同,采用多个尺度,通过最小化当前训练误差,选择最佳的核中心和尺度参数。在学习过程中,采用非正交核函数的方法进行模型逐步回归。对样本数据利用k均值聚类算法得到核函数中心参数备选项,同时设置多个备选尺度,并通过最小二乘法求得相应核函数的权值,利用前向选择方法从中找出使模型误差最小的最优核函数。仿真实验验证了方法在泛化性能和稀疏性方面的可行性。
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.
文摘针对SI(Spark Ignition)发动机空燃比(AFR:Air-Fuel Ratio)控制精度低、无法自适应等问题,提出了基于NARX(Nonlinear Auto Regressive model with e Xogenous inputs)模型的非线性模型预测控制(NMPC:Nonlinear Model Predict Control)方法。利用渐消记忆递推最小二乘(RLS:Recursive Least Squares)算法对NARX模型进行辨识,基于NARX模型对SI发动机的AFR进行非线性模型预测控制。该方法辨识精度高,可通过NARX模型数学结构直接计算最优控制序列,从而提高系统的控制精度。同时,采用Matlab对均值发动机模型(MVEM:Mean Value Engine Model)进行仿真实验,并与采用Volterra模型的PI(Proportional Integral)控制器算法进行对比。仿真结果证明,该算法控制效果比基于Volterra模型和传统的PI控制器的控制效果超调量小,调节时间短,更加具有工程实际应用性。
