Sufficient statistics for unobserved heterogeneity in structural dynamic logit models

In this study, a novel easy‐to‐use meta‐heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

Identification of structural vibration parameters by using a frequency domain filter

The accurate identification and characterization of the accelerometer dynamic model parameters play an important role in improving the dynamic performance of the device or system with an accelerometer. To overcome the problem that the traditional single degree of freedom (SDF) dynamic model of the accelerometer cannot describe the dynamic characteristics beyond the first resonant frequency of the accelerometer, a two degree of freedom (TDF) dynamic model of the accelerometer was constructed. On this basis, a parameter identification method for the TDF dynamic model of the accelerometer based on the feature points coordinate estimation and amplitude correction was proposed. First, the zero frequency point coordinates of the accelerometer frequency response were obtained by the Hv method. The first and second resonance point coordinates were estimated by discrete spectrum correction and the least square (DSC-LS) method. Then, the amplitude correction coefficient was applied to eliminate the influence of series coupling on the amplitude. Finally, the TDF dynamic model parameters of the accelerometer were calculated through the feature point coordinates. The experimental results show that the method has high accuracy and can avoid the influence of series coupling on the parameter identification accuracy of the accelerometer’s TDF dynamic model without complex derivation and decoupling operations. The identified TDF dynamic model of the accelerometer can represent the dynamic characteristics with a higher frequency range.

Fuzzy neural networks have both the interpretability of fuzzy systems and the self-learning ability of neural networks, but they will face the challenge of “rule explosion” when dealing with high-dimensional data. Moreover, the structure and parameter identifications of models are generally performed in two stages, and this always attends to one thing and loses another in terms of interpretability and predictive performance. In this paper, a fuzzy neural network regression method (FNNR) that coordinates structure identification and parameter identification is proposed. To alleviate the problem of rule explosion, the structure identification and parameter identification are coordinated in the training process, and the numbers of fuzzy rules and fuzzy partitions are effectively limited, while the parameters of fuzzy rules are optimized. The symmetrical architecture of the FNNR is designed for automatic structure identification. An alternate training strategy is adopted by treating discrete and continuous parameters differently, and thus the convergence efficiency of the algorithm is improved. To enhance interpretability, regularized terms are designed from fuzzy rule level and fuzzy partition level to guide the model to learn fuzzy rules with simple structures and clear semantics. The experimental results show that the proposed method has both a compact structure and high precision.

This paper aims at applying the adaptive Tikhonov regularization to realize the simultaneous identification of structural physical parameters and input based on dynamic response sensitivity. The theories of the simultaneous identification algorithm and the adaptive Tikhonov regularization were first briefly introduced, and then a twelve story shear frame was studied for validating the proposed method. Numerical simulations with noisy measured accelerations show that the proposed method can accurately identify structural parameters and unknown support excitation from only several responses of the structure, and this method can provide a new approach for structural model updating or damage detection with unknown input and incomplete measured output information.

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

This study investigates stochastic income convergence across Italian regions at two spatial scales—NUTS-2 (regions) and NUTS-3 (provinces)—in a context of an affluent North and the less-developed South. Using dynamic panel data models, both non-spatial and spatial, for 2001–2021, the analysis explores the roles of persistence, unobserved regional heterogeneity, and spatial dependence in income gap dynamics. Models without fixed effects show near-unit-root behavior, indicating high persistence or divergence, whereas including fixed effects substantially reduces persistence, providing evidence of conditional stochastic convergence at both spatial scales. Spatial dependence is limited at the NUTS-2 level, with coefficients generally small and insignificant, but at the NUTS-3 level, spatial interactions are economically meaningful and significant, highlighting localized spillovers in provincial income disparities. Ignoring these interactions at the finer scale can overstate persistence. To correct for Nickell bias in dynamic fixed-effects models, System GMM estimators are employed. Spatial System GMM results show strong but stable persistence, with coefficients around 0.86–0.87. The implied half-life of income shocks is roughly five years at the regional level and four to four-and-a-half years at the provincial level, indicating faster adjustments locally. Overall, the study finds weak but robust conditional stochastic convergence, emphasizing the importance of accounting for both spatial dependence and unobserved heterogeneity in understanding regional income dynamics in Italy.Unlike previous studies, our results quantify the persistence and half-life of income shocks, highlighting the difference between NUTS-2 and NUTS-3

A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

This paper presents a frequency domain identification of dynamic model parameters for frictional presliding behavior. The identification procedure for the dynamic model parameters, i.e., (1) the stiffness and (2) the damping of the presliding phenomenon, is reduced from performing several dedicated experiments to one experiment where the system is excited with random noise and the frequency response function (FRF) of the phenomenon is measured. Time domain validation experiments on a servomechanism show accurate estimates of the dynamic model parameters for the linearized presliding behavior.

Frequency-Domain Gray-Box Identification of Industrial Robots

Real‐Time System Identification: An Algorithm for Simultaneous Model Class Selection and Parametric Identification

A structural parametric identification strategy based on neural networks algorithms using dynamic macro-strain measurements in time domain from a long-gage strain sensor by fiber optic sensing technique such as Fiber Bragg Grating (FBG) sensor is developed. An array of long-gage sensors is bounded on the structure to measure reliably and accurately macro-strains. By the proposed methodology, the structural parameter of stiffness can be identified. A beam model with known mass distribution is considered as an object structure. Without any eigenvalue analysis or optimization computation, the structural parameter of stiffness can be identified. First an emulator neural network is presented to identify the beam structure in current state. Free vibration macro-strain responses of the beam structure are used to train the emulator neural network. The trained emulator neural network can be used to forecast the free vibration macro-strain response of the beam structure with enough precision and decide the difference between the free vibration macro-strain responses of other assumed structure with different structural parameters and those of the original beam structure. The root mean square (RMS) error vector is presented to evaluate the difference. Subsequently, corresponding to each assumed structure with different structural parameters, the RMS error vector can be calculated. By using the training data set composed of the structural parameters and RMS error vector, a parametric evaluation neural network is trained. A beam structure is considered as an existing structure, based on the trained parametric evaluation neural network, the stiffness of the beam structure can be forecast. It is shown that the parametric identification strategy using macro-strain measurement from long-gage sensors has the potential of being a practical tool for a health monitoring methodology applied to civil engineering structures.

Modelling of voltage dependent load is always a current topic due to existence of an adequate load model is a prerequisite for calculation of steady states and the analysis of transient regimes of power systems. Given that it is important to know the load model parameters, we have performed the measurements in electric power system of Bosnia and Herzegovina (EPS BIH). The main idea is to identify the values of voltage parameters of dynamic load model. Therefore, this paper describes the dynamic load models and the methodology for exponential dynamic load model parameter identification. It also presents the voltage parameters of dynamic load model for a node in EPS BIH and compares them to similar published studies, concluding that the difference in given parameter values is the consequence of local load characteristics.

Kinematic analysis and error modeling of TAU parallel robot

The TAU parallel configuration is rooted in a series of inventions and was masterminded by Torgny Brogardh [1][2][3][4]. The configuration of the robot simulates the shape of “τ ” like the name of the Delta after the “∇ ” shape configuration of another parallel robot. As shown in Fig. 1, the basic TAU configuration consists of 3 driving axes, 3 arms, 6 linkages, 12 joints and a moving (tool) plate. There are 6 chains connecting the main column to the end-effector in TAU configuration. The TAU robot is a typical 3/2/1 configuration. There are 3 parallel and identical links and another 2 parallel and identical links. Six chains will be used to derive all kinematic equations. Table 1 highlights the features of the TAU configuration. On the subject of D-H modeling, Tasi [5], Raghavan [6], Abderrahim and Whittaker[12] have applied the method and studied the limitations of various modeling methods. On the subject of forward kinematics, focus has been on finding closed form solutions based on various robotic configurations, and numerical solutions for difficult configurations of robots. It can be found in the work done by Dhingra [8], Shi [14], Didrit [16], Zhang [17], Nanua [18], Sreenivasan [19], Griffis and Duffy [20], Lin [21]. On the subject of error analysis, Wang and Masory [7], Gong [11], Patel and Ehmann [13] used forward solutions to obtain errors. Jacobian matrix was also used in obtaining errors. On the subject of the variation of parallel configurations, from the work done by Dhingra [9][ 10], Geng and Haynes [15], the influence of the configurations on the methods of finding closed form solutions can be found. In this paper, the D-H model is used to define the TAU robot, a complete set of parameters is included in the modeling process. Kinematic modeling and error modeling are established with all errors using Jacobian matrix method for the TAU robot. Meanwhile, a very effective Jacobian Approximation Method is introduced to calculate the forward kinematic problem instead of Newton-Raphson method. It denotes that a closed form solution can be obtained instead of a numerical solution. A full size Jacobian matrix is used in carrying out error analysis, error budget, and model parameter estimation and identification. Simulation results indicate that both Jacobian matrix and Jacobian Approximation Method are correct and have an accuracy of micron meters. ADAMS simulation results are used in verifying the established models.