Autocorrelation model-based identification method for ARMA systems in noise

2005 ◽  
Vol 152 (5) ◽  
pp. 520 ◽  
Author(s):  
M.K. Hasan ◽  
N.M. Hossain ◽  
P.A. Naylor
Keyword(s):  
Identification Method ◽  
Model Based

Non-model-based damage identification of plates using measured mode shapes

2016 ◽  
Vol 16 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Yongfeng Xu ◽  
Weidong Zhu
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Damage Index ◽  
Thickness Reduction ◽  
Aluminum Plate ◽  
Mode Shapes ◽  
Identification Method ◽  
Model Based

Mode shapes (MSs) have been extensively used to detect structural damage. This paper presents a new non-model-based damage identification method that uses measured MSs to identify damage in plates. A MS damage index (MSDI) is proposed to identify damage near regions with consistently high values of MSDIs associated with MSs of different modes. A MS of a pseudo-undamaged plate can be constructed for damage identification using a polynomial of a properly determined order that fits the corresponding MS of a damaged plate, if the associated undamaged plate is geometrically smooth and made of materials that have no stiffness and mass discontinuities. It is shown that comparing a MS of a damaged plate with that of a pseudo-undamaged plate is better for damage identification than with that of an undamaged plate. Effectiveness and robustness of the proposed method for identifying damage of different positions and areas are numerically investigated using different MSs; effects of crucial factors that determine effectiveness of the proposed method are also numerically investigated. Damage in the form of a machined thickness reduction area was introduced to an aluminum plate; it was successfully identified by the proposed method using measured MSs of the damaged plate.

A new identification method of the Stribeck friction model based on limit cycles

2014 ◽  
Vol 228 (15) ◽  
pp. 2678-2683 ◽  
Author(s):  
LL Liu ◽  
ZY Wu
Keyword(s):  
Parameter Identification ◽  
Limit Cycle ◽  
Limit Cycles ◽  
Friction Model ◽  
Limit Cycle Oscillation ◽  
Characteristic Parameters ◽  
Chain Code ◽  
Shape Characteristic ◽  
Identification Method ◽  
Model Based

This paper presents a new parameter identification method of the Stribeck friction model based on limit cycles. A single degree of freedom mass spring system driven by a belt is studied, and the Stribeck friction model is established between the mass and belt. Limit cycle oscillation will occur when the system is unstable. The limit cycle curve is described by some main shape characteristic parameters using the modified Freeman chain code method. Thus, the Stribeck friction parameters can be identified by using the ergodic search method to minimize the Euclidean distance of the theoretical and identified limit cycle shape characteristic parameters. The parameter identification method based on limit cycles is different from the traditional identification methods. It only needs the displacement and velocity responses of the system instead of the measurement of the friction force or motor voltage/current. All of these works can provide the reference for the research work of the friction parameter identification.

Note: Model-based identification method of a cable-driven wearable device for arm rehabilitation

2015 ◽  
Vol 86 (9) ◽  
pp. 096107 ◽  
Author(s):  
Xiang Cui ◽  
Weihai Chen ◽  
Jianbin Zhang ◽  
Jianhua Wang
Keyword(s):  
Wearable Device ◽  
Identification Method ◽  
Model Based ◽  
Arm Rehabilitation

An Impedance Model-Based Multiparameter Identification Method of PMSM for Both Offline and Online Conditions

2021 ◽  
Vol 36 (1) ◽  
pp. 727-738
Author(s):  
Qiwei Wang ◽  
Gaolin Wang ◽  
Nannan Zhao ◽  
Guoqiang Zhang ◽  
Qingwen Cui ◽  
...  
Keyword(s):  
Impedance Model ◽  
Identification Method ◽  
Model Based

scholarly journals A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 18251-18263 ◽  
Author(s):  
Jikui Liu ◽  
Liyan Yin ◽  
Chenguang He ◽  
Bo Wen ◽  
Xi Hong ◽  
...  
Keyword(s):  
Autoregressive Model ◽  
Identification Method ◽  
Model Based

scholarly journals Neural network based identification of hysteresis in human meridian systems

2012 ◽  
Vol 22 (3) ◽  
pp. 685-694 ◽  
Author(s):  
Yonghong Tan ◽  
Ruili Dong ◽  
Hui Chen ◽  
Hong He
Keyword(s):  
Neural Network ◽  
Dynamic System ◽  
Hysteresis Phenomenon ◽  
Input Space ◽  
Output State ◽  
Identification Method ◽  
Model Based ◽  
Learning Machine ◽  
Scheme Evaluation ◽  
Digital Human

Abstract Developing a model based digital human meridian system is one of the interesting ways of understanding and improving acupuncture treatment, safety analysis for acupuncture operation, doctor training, or treatment scheme evaluation. In accomplishing this task, how to construct a proper model to describe the behavior of human meridian systems is one of the very important issues. From experiments, it has been found that the hysteresis phenomenon occurs in the relations between stimulation input and the corresponding response of meridian systems. Therefore, the modeling of hysteresis in a human meridian system is an unavoidable task for the construction of model based digital human meridian systems. As hysteresis is a nonsmooth, nonlinear and dynamic system with a multi-valued mapping, the conventional identification method is difficult to be employed to model its behavior directly. In this paper, a neural network based identification method of hysteresis occurring in human meridian systems is presented. In this modeling scheme, an expanded input space is constructed to transform the multi-valued mapping of hysteresis into a one-to-one mapping. For this purpose, a modified hysteretic operator is proposed to handle the extremum-missing problem. Then, based on the constructed expanded input space with the modified hysteretic operator, the so-called Extreme Learning Machine (ELM) neural network is utilized to model hysteresis inherent in human meridian systems. As hysteresis in meridian system is a dynamic system, a dynamic ELMneural network is developed. In the proposed dynamic ELMneural network, the output state of each hidden neuron is fed back to its own input to describe the dynamic behavior of hysteresis. The training of the recurrent ELM neural network is based on the least-squares algorithm with QR decomposition.

scholarly journals An Asymmetric Hysteresis Model and Parameter Identification Method for Piezoelectric Actuator

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Haichen Qin ◽  
Ningbin Bu ◽  
Wei Chen ◽  
Zhouping Yin
Keyword(s):  
Parameter Identification ◽  
Smart Materials ◽  
Model Parameters ◽  
Hysteresis Model ◽  
Pzt Actuator ◽  
Modified Particle Swarm Optimization ◽  
Identification Method ◽  
Model Based ◽  
Asymmetric Factor ◽  
Hysteresis Behaviour

Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input biasφand an asymmetric factorΔΦinto the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO) algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour.

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