A novel method based on maximum likelihood estimation for the construction of seismic fragility curves using numerical simulations

Seismic fragility curves presenting some probability of failure or of a damage state exceedance versus seismic intensity can be established by engineering judgment, empirical or numerical approaches. This paper focuses on the latter issue. In recent studies, three popular methods based on numerical simulations, comprising scaled seismic intensity, maximum likelihood estimation and probabilistic seismic demand/capacity models, have been studied and compared. The results obtained show that the maximum likelihood estimation (MLE) method is in general better than other ones. However, previous publications also indicated the dependence of the MLE method on the ground excitation input. The objective of this paper is thus to propose a novel method improving the existing MLE one. Improvements are based on probabilistic ground motion information, which is taken into account in the proposed procedure. The validity of this new approach is verified by analytical tests and numerical examples.     

Maximum likelihood Newton recursive and the Newton iterative estimation algorithms for Hammerstein CARAR systems

This paper discusses the identification problems of Hammerstein controlled autoregressive autoregressive (CARAR) systems using the maximum likelihood principle and Newton optimization method. A Newton recursive algorithm and a Newton iterative algorithm using the maximum likelihood principle are presented. The simulation results show that the proposed algorithms can effectively estimate the parameters of the Hammerstein CARAR systems.     

MILM hybrid identification method of fractional order neural-fuzzy Hammerstein model

Aiming at the difficult identification of fractional order Hammerstein nonlinear systems, including many identification parameters and coupling variables, unmeasurable intermediate variables, difficulty in estimating the fractional order, and low accuracy of identification algorithms, a multiple innovation Levenberg–Marquardt algorithm (MILM) hybrid identification method based on the fractional order neuro-fuzzy Hammerstein model is proposed. First, a fractional order discrete neuro-fuzzy Hammerstein system model is constructed; secondly, the neuro-fuzzy network structure and network parameters are determined based on fuzzy clustering, and the self-learning clustering algorithm is used to determine the antecedent parameters of the neuro-fuzzy network model; then the multiple innovation principle is combined with the Levenberg–Marquardt algorithm, and the MILM hybrid algorithm is used to estimate the linear module parameters and fractional order. Finally, the academic example of the fractional order Hammerstein nonlinear system and the example of a flexible manipulator are identified to prove the effectiveness of the proposed algorithm.

     

Hierarchical recursive least squares algorithm for Hammerstein systems using the filtering method

A hierarchical recursive least squares algorithm is presented in the paper to estimate the parameters of Hammerstein nonlinear systems by combining the filtering method and least squares search principle. The key is to decompose the Hammerstein system into two subsystems by adopting the hierarchical idea. Numerical examples are given to illustrate the performance of the proposed algorithm.     

A sliding-window approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systems

Nonlinear Dynamics - This paper presents a sliding-window approximation-based fractional least mean square (FLMS) algorithm for parameter estimation of Hammerstein nonlinear autoregressive moving...     

A novel method for modeling skidding for systems with nonholonomic constraints

In this paper, the effect of wheel skidding on the steering motion of a simple vertical rolling disk is investigated. By modifying the nonholonomic constraints, two novel dynamic models are proposed. The first model rotates the constraints and enforces them along a plane correlated to the skid angle. It then relates the skidding in a wheel to the Lagrange multipliers associated with the kinematic constraints of that wheel. The second model relaxes the no-skidding constraint, allowing its transgression and relates the skidding to the generalized velocities of the actuated degrees of freedom of the system. To validate our model, we compare it to one in the literature and we analyze the motion of the disk on icy and snowy road conditions, where skidding can be significant.     

A novel embedding method for characterization of low-dimensional nonlinear dynamical systems

Nonlinear Dynamics - Characterization of dynamical systems remains a central challenge in real-world applications because, in most cases, governing equations of the systems cannot be obtained...     

Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.     

A maximum principle for the analysis of elastic-plastic systems

Summary We consider an elastic-plastic body subjected to a specific programme of static loading. In the analysis of the behaviour of the body in the course of the individual steps of the loading programme some difficulties arise if the variational principles of the theory of plasticity are applied. We then propose a maximum principle which appears suitable for the formulation of simple and direct computing procedures. For an elastic-perfectly plastic material the function to be maximized represents the differential energy dissipated in the single infinitesimal step starting from the elastic solution. In that function the variables are the plastic distortions. Since the energy dispersed by the effect of these latter must at every point be positive or zero, the maximum in question is a field maximum and therefore the property is not variational.The principle is demonstrated both for elastic-perfectly plastic materials and for elastic—work-hardening materials. Materials with regular yield surfaces are at first considered; the demonstration is then extended to the case of singular yield surfaces.First published in Rendiconti dell'Istituto Lombardo, Classe di Scienze, A 99, 125–140, 1965.     

Online system identification using fractional-order Hammerstein model with noise cancellation

Slow convergence and low accuracy are two main drawbacks in nonlinear system identification methods. It becomes more complicated when time delay and noises are considered. In this paper, considering a fractional-order Hammerstein model, an online identification method is proposed. A combination of an evolutionary optimization method and recursive least square algorithm is used to estimate the system parameters and orders in the presence of unknown noises. Finally, simulation results are taken to prove the effectiveness of the proposed algorithm.

     

基于极大似然比的多故障卫星完好性检测

针对多星座情况下多卫星同时故障时的接收机自主完好性检测的问题,分析了多卫星同时故障的原因及特点,提出基于极大似然比的分层完好性检测方法.通过奇偶向量矩阵的计算,根据极大似然估计,进行故障检测与隔离,利用全量检验统计值与部分检验统计值之间的关系进行故障卫星的确定,并利用接收机的数据进行仿真验证.仿真结果表明,本方法可以快速有效地实现多星座情况下的接收机自主完好性检测,检测出并隔离故障卫星.     

一种结构参数识别的两阶段方法

针对测量信息不完备的剪切型结构 ,建立了一种两阶段系统识别的复合反演方法 ,这种方法包括两部分 :子结构地震动反演和结构参数识别。首先 ,选取可观测的子结构 ,利用一维地震动作用于结构的力学特性 ,将子结构动力方程的有限元列式进行变换 ,得到适合于最小二乘法的简单形式 ,解决了测量信息不完备及结构参数未知条件下的地震动反演问题。其次 ,根据子结构反演得到的地震动输入 ,采用结构参数时域识别技术中的加权整体迭代 -广义卡尔曼滤波器方法 ,成功地识别出了有限测量条件下单元水平结构参数     

A wavelet deconvolution method for impact force identification

The inverse problem of solving for impact force history using experimentally measured structural responses tends to be ill conditioned. A computationally efficient deconvolution method with similarities to Fourier analysis and wavelet analysis is introduced. Force reconstructions obtained using measured acceleration responses from beam and plate models are used to verify the method.