Parameter Identification from Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm

2021 ◽  
Author(s):  
Benjamin Sackmann ◽  
Peter Eberhard ◽  
Michael Lauxmann
Keyword(s):  
Parameter Identification ◽  
Middle Ear ◽  
Measurement Data ◽  
High Sensitivity ◽  
Element Model ◽  
Great Sensitivity ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Good Representation ◽  
Adaptive Parameter

Abstract Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is therefore fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics, and address the challenges of limited measurement data and wide parameter ranges from literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals ONLINE PARAMETER IDENTIFICATION OF RICE TRANSPLANTER MODEL BASED ON IPSO-EKF ALGORITHM

2020 ◽  
Vol 61 (2) ◽  
pp. 25-34 ◽  
Author(s):  
Yibo Li ◽  
Hang Li ◽  
Xiaonan Guo
Keyword(s):  
Parameter Identification ◽  
Measured Data ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Output Data ◽  
Swarm Optimization ◽  
Model Based ◽  
Simulation Results ◽  
Filtering Effect ◽  
Ipso Algorithm

In order to improve the accuracy of rice transplanter model parameters, an online parameter identification algorithm for the rice transplanter model based on improved particle swarm optimization (IPSO) algorithm and extended Kalman filter (EKF) algorithm was proposed. The dynamic model of the rice transplanter was established to determine the model parameters of the rice transplanter. Aiming at the problem that the noise matrices in EKF algorithm were difficult to select and affected the best filtering effect, the proposed algorithm used the IPSO algorithm to optimize the noise matrices of the EKF algorithm in offline state. According to the actual vehicle tests, the IPSO-EKF was used to identify the cornering stiffness of the front and rear tires online, and the identified cornering stiffness value was substituted into the model to calculate the output data and was compared with the measured data. The simulation results showed that the accuracy of parameter identification for the rice transplanter model based on the IPSO-EKF algorithm was improved, and established an accurate rice transplanter model.

Parameter Identification for Nonlinear Dry-Friction Structure of Metallic Rubber

2011 ◽  
Vol 52-54 ◽  
pp. 494-499
Author(s):  
Yu Yan Li ◽  
Xie Qing Huang ◽  
Kai Song
Keyword(s):  
Parameter Identification ◽  
Dry Friction ◽  
Friction Angle ◽  
Element Model ◽  
Parameters Identification ◽  
Axial Stiffness ◽  
Model Parameters ◽  
Structural Element ◽  
Identification Method ◽  
Metallic Rubber

In order to reduce workload of parameter identification for nonlinear mechanical model of metallic rubber, in this paper, based on parameters identification method of static experimental curves, experiments were designed, and data were processed, further aimed at hollow cylindrical metallic rubber, nonlinear dry-friction structural element model’ parameters were identified, what’s more, friction coefficient, radial stiffness, axial stiffness, and friction angle of stainless wire under room temperature were obtained. It was proved by simulation that parameters identification method in this paper was effective and accurate. Based on this, errors of simulation were analyzed elaborately.

Material parameter identification in functionally graded structures using isoparametric graded finite element model

2016 ◽  
Vol 23 (6) ◽  
pp. 685-698 ◽  
Author(s):  
Lixin Huang ◽  
Ming Yang ◽  
Xiaojun Zhou ◽  
Qi Yao ◽  
Lin Wang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Parameter Identification ◽  
Material Parameter ◽  
Element Model ◽  
Functionally Graded ◽  
Identification Algorithm ◽  
Material Parameter Identification ◽  
Measured Displacement ◽  
Graded Structures

AbstractAn identification algorithm based on an isoparametric graded finite element model is developed to identify the material parameters of the plane structure of functionally graded materials (FGMs). The material parameter identification problem is formulated as the problem of minimizing the objective function, which is defined as a square sum of differences between measured displacement and calculated displacement by the isoparametric graded finite element approach. The minimization problem is solved by using the Levenberg-Marquardt method, in which the sensitivity calculation is based on the differentiation of the governing equations of the isoparametric graded finite element model. The validity of this algorithm is illustrated by some numerical experiments. The numerical results reveal that the proposed algorithm not only has high accuracy and stable convergence, but is also robust to the effects of measured displacement noise.

Parameter identification of a differentiable Bouc-Wen model using constrained extended Kalman filter

2020 ◽  
pp. 147592172092943
Author(s):  
Dan Li ◽  
Yang Wang
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Extended Kalman Filter ◽  
Structural Safety ◽  
Model Parameters ◽  
Suitable Model ◽  
Identification Algorithm ◽  
Hysteretic Model ◽  
System Equations ◽  
Parameter Constraints

Hysteresis is of critical importance to structural safety under severe dynamic loading conditions. One of the widely used hysteretic models for civil structures is the Bouc-Wen model, the effectiveness of which depends on suitable model parameters. The locally non-differentiable governing equation of the conventional Bouc-Wen model poses difficulty on existing identification algorithms, especially the extended Kalman filter, which relies on linearized system equations to propagate state estimates and covariance. In addition, the standard extended Kalman filter usually does not incorporate parameter constraints, and therefore may result in unreasonable estimates. In this article, a modified and differentiable Bouc-Wen model, together with a constrained extended Kalman filter (CEKF), is proposed to identify the hysteretic model parameters in a reliable way. The partial derivatives of the differentiable Bouc-Wen model with respect to hysteretic parameters can be easily calculated for implementing the identification algorithm. Constrained extended Kalman filter restricts the Kalman gain to ensure that the estimates of parameters satisfy constraints from physical laws. Parameter identification using simulated and experimental data collected from a four-story structure demonstrates that constrained extended Kalman filter can achieve more reliable identification results than the standard extended Kalman filter.

scholarly journals Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3180 ◽  
Author(s):  
Bizhong Xia ◽  
Rui Huang ◽  
Zizhou Lao ◽  
Ruifeng Zhang ◽  
Yongzhi Lai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Lithium Ion ◽  
Least Square ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Temperature State

The model parameters of the lithium-ion battery are of great importance to model-based battery state estimation methods. The fact that parameters change in different rates with operation temperature, state of charge (SOC), state of health (SOH) and other factors calls for an online parameter identification algorithm that can track different dynamic characters of the parameters. In this paper, a novel multiple forgetting factor recursive least square (MFFRLS) algorithm was proposed. Forgetting factors were assigned to each parameter, allowing the algorithm to capture the different dynamics of the parameters. Particle swarm optimization (PSO) was utilized to determine the optimal forgetting factors. A state of the art SOC estimator, known as the unscented Kalman filter (UKF), was combined with the online parameter identification to create an accurate estimation of SOC. The effectiveness of the proposed method was verified through a driving cycle under constant temperature and three different driving cycles under varied temperature. The single forgetting factor recursive least square (SFFRLS)-UKF and UKF with fixed parameter were also tested for comparison. The proposed MFFRLS-UKF method obtained an accurate estimation of SOC especially when the battery was running in an environment of changing temperature.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

scholarly journals Model parameter identification from measurement data for dynamic torque calibration – Measurement results and validation

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (3) ◽  
pp. 55 ◽  
Author(s):  
Leonard Klaus
Keyword(s):  
Parameter Identification ◽  
Transfer Functions ◽  
Dynamic Properties ◽  
Measurement Data ◽  
Model Parameters ◽  
Dynamic Calibration ◽  
Measuring Device ◽  
Model Parameter ◽  
Calibration Measurement ◽  
Model Parameter Identification

<p><span lang="EN-US">The dynamic calibration of torque transducers requires the </span><span lang="EN-GB">modelling</span><span lang="EN-US"> of the measuring device and of the transducer under test. The transducer's dynamic properties are described by means of model parameters, which are going to be identified from measurement data. To be able to do so, two transfer functions are calculated. In this paper, the transfer functions and the procedure for the model parameter identification are presented. Results of a parameter identification of a torque transducer are also given, and the validity of the identified parameters is </span><span lang="EN-GB">analysed</span><span lang="EN-US"> by comparing the results with independent measurements. The successful parameter identification is a prerequisite for a model-based dynamic calibration of torque transducers.</span></p>

Algebraic Driver Steering Model Parameter Identification

2022 ◽  
Author(s):  
Zejiang Wang ◽  
Xingyu Zhou ◽  
Heran Shen ◽  
Junmin Wang
Keyword(s):  
Parameter Identification ◽  
Driving Simulator ◽  
Measurement Data ◽  
Algebraic Method ◽  
Recursive Least Squares ◽  
Superior Performance ◽  
Model Parameters ◽  
Driver Assistance Systems ◽  
Short Period ◽  
Dynamics And Control

Abstract Modeling driver steering behavior plays an ever-important role in nowadays automotive dynamics and control applications. Especially, understanding individuals' steering characteristics enables the advanced driver assistance systems (ADAS) to adapt to particular drivers, which provides enhanced protection while mitigating human-machine conflict. Driver-adaptive ADAS requires identifying the parameters inside a driver steering model in real-time to account for driving characteristics variations caused by weather, lighting, road, or driver physiological conditions. Usually, Recursive Least Squares (RLS) and Kalman Filter (KF) are employed to update the driver steering model parameters online. However, because of their asymptotical nature, the convergence speed of the identified parameters could be slow. In contrast, this paper adopts a purely algebraic perspective to identify parameters of a driver steering model, which can achieve parameter identification within a short period. To demonstrate the effectiveness of the proposed method, we first apply synthetic driver steering data from simulation to show its superior performance over an RLS identifier in identifying constant model parameters, including feedback steering gain, feedforward steering gain, preview time, and first-order neuromuscular lag. Then, we utilize real measurement data from human subject driving simulator experiments to illustrate how the time-varying feedback and feedforward steering gains can be updated online via the algebraic method.

scholarly journals Combined approach for optimal sensor placement and experimental verification in the context of tower-like structures

2020 ◽  
Author(s):  
Ina Reichert ◽  
Peter Olney ◽  
Tom Lahmer
Keyword(s):  
Model Updating ◽  
Mean Squared Error ◽  
Information Matrix ◽  
Measurement Data ◽  
Element Model ◽  
Computational Time ◽  
Model Parameters ◽  
Combined Approach ◽  
Step Procedure ◽  
Sensor Positions

AbstractWhen it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures’ models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure’s behavior and model parameters without the need of preliminary measurements for model updating.

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