scholarly journals Identifying the connective strength between model parameters and performance criteria

2017 ◽  
Author(s):  
Björn Guse ◽  
Matthias Pfannerstill ◽  
Abror Gafurov ◽  
Jens Kiesel ◽  
Christian Lehr ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Latin Hypercube Sampling ◽  
Performance Criteria ◽  
Joint Analysis ◽  
Model Parameters ◽  
Flow Conditions ◽  
Target Variable ◽  
Model Parameter ◽  
And Performance ◽  
Three Components

Abstract. In hydrological models, parameters are used to adapt the model to the conditions of the catchments. Hereby, the parameters need to be identified based on their role in controlling the hydrological behaviour in the model. For parameter identification, multiple and complementary performance criteria are used, which have to capture the different aspects of hydrological response of catchments. A reliable parameter identification depends on how distinctly a model parameter can be assigned to one of the performance criteria. We introduce an analysis that reveals the connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the inter-relationship between model parameters and performance criteria. In our analysis of connective strength, model simulations are carried out based on a Latin Hypercube sampling. Ten performance criteria in cluding the NSE, the KGE and its three components (alpha, beta and r) as well as the RSR for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression trees (RT), it is derived how a model parameter is connected to the different performance criteria. At first, RTs are constructed using each performance criteria as target variable to detect the most relevant model parameters for each performance criteria. A second RT approach using each parameter as target variable detects which performance criterion is impacted by changes in parameter values. Based on this, appropriate performance criteria are identified for each model parameter. A high bijective connective strength is calculated for low and mid flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of the KGE and of the FDC segments. It is emphasised under which conditions these performance criteria provide insights into a precise parameter identification. Separate performance criteria are required to identify dominant parameters on low and mid flow conditions, whilst the number of required performance criteria for high flows increases with the process complexity in the catchment. Overall, the analysis of the connective strength using RTs contribute towards a better handling of parameters and performance criteria in hydrological modelling.

scholarly journals Identifying the connective strength between model parameters and performance criteria

2017 ◽  
Vol 21 (11) ◽  
pp. 5663-5679 ◽  
Author(s):  
Björn Guse ◽  
Matthias Pfannerstill ◽  
Abror Gafurov ◽  
Jens Kiesel ◽  
Christian Lehr ◽  
...  
Keyword(s):  
Performance Criterion ◽  
Performance Criteria ◽  
Joint Analysis ◽  
Model Parameters ◽  
Flow Conditions ◽  
Target Variable ◽  
Model Parameter ◽  
And Performance ◽  
Parameter Values ◽  
Three Components

Abstract. In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria. To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter. In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.

Carbon Brake Friction Model Parameter Identification Using Genetic Algorithms

1995 ◽  
Author(s):  
Roger C. von Doenhoff ◽  
Robert J. Streifel ◽  
Robert J. Marks
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Surface Temperature ◽  
Parameter Identification ◽  
Friction Model ◽  
Carbon Surface ◽  
Model Parameters ◽  
Model Parameter ◽  
Brake Pressure ◽  
Model Parameter Identification

Abstract A model of the friction characteristics of carbon brakes is proposed to aid in the understanding of the causes of brake vibration. The model parameters are determined by a genetic algorithm in an attempt to identify differences in friction properties between brake applications during which vibration occurs and those during which there is no vibration. The model computes the brake torque as a function of wheelspeed, brake pressure, and the carbon surface temperature. The surface temperature is computed using a five node temperature model. The genetic algorithm chooses the model parameters to minimize the error between the model output and the torque measured during a dynamometer test. The basics of genetic algorithms and results of the model parameter identification process are presented.

Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

A UAV MODEL PARAMETER IDENTIFICATION METHOD: A SIMULATION STUDY

2009 ◽  
Vol 06 (04) ◽  
pp. 225-238 ◽  
Author(s):  
K. S. HATAMLEH ◽  
O. MA ◽  
R. PAZ
Keyword(s):  
Parameter Identification ◽  
Control Strategies ◽  
Least Square ◽  
Model Parameters ◽  
Dynamics Modeling ◽  
Recursive Least Square ◽  
Nonlinear Dynamical ◽  
Model Parameter ◽  
Identification Method ◽  
Model Parameter Identification

Dynamics modeling of Unmanned Aerial Vehicles (UAVs) is an essential step for design and evaluation of an UAV system. Many advanced control strategies for nonlinear dynamical or robotic systems which are applicable to UAVs depend upon known dynamics models. The accuracy of a model depends not only on the mathematical formulae or computational algorithm of the model but also on the values of model parameters. Many model parameters are very difficult to measure for a given UAV. This paper presents the results of a simulation based study of an in-flight model parameter identification method. Assuming the motion state of a flying UAV is directly or indirectly measureable, the method can identify the unknown inertia parameters of the UAV. Using the recursive least-square technique, the method is capable of updating the model parameters of the UAV while the vehicle is in flight. A scheme of estimating an upper bound of the identification error in terms of the input data errors (or sensor errors) is also discussed.

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>

Study on the Giant Magnetostrictive Materials with Actuator Displacement Model Parameter Identification

2013 ◽  
Vol 830 ◽  
pp. 37-40
Author(s):  
Jia Deng
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parameter Identification ◽  
Nonlinear Model ◽  
Simulated Annealing Algorithm ◽  
Model Parameters ◽  
Accurate Identification ◽  
Model Parameter ◽  
Annealing Algorithm ◽  
Model Parameter Identification

Accurate identification of model parameters is to improve the giant magnetostrictive precision displacement control key, For single algorithm is difficult to achieve for giant magnetostrictive hysteresis nonlinear model parameters accurately identify problems, in this paper, the genetic algorithm and simulated annealing algorithm fusion, First, quick search ability of genetic algorithm are used to get a better community, recycle kick ability of simulated annealing algorithm to to adjust and optimize the whole group, Presented an improved genetic simulated annealing algorithm, And its application to the giant magnetostrictive actuator displacement hysteresis nonlinear model parameter identification. The algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, both the faster convergence speed, and improves the precision and quality of the optimal solution.

A dynamic model parameter identification method for quadrotors using flight data

2018 ◽  
Vol 233 (6) ◽  
pp. 1990-2002 ◽  
Author(s):  
Pin Lyu ◽  
Sheng Bao ◽  
Jizhou Lai ◽  
Shichao Liu ◽  
Zang Chen
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Unmanned Aerial Vehicle ◽  
Model Parameters ◽  
Rolling Moment ◽  
Flight Data ◽  
Model Parameter ◽  
Identification Method ◽  
Aerial Vehicle ◽  
Model Parameter Identification

The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as measurements. The observabilities of the model parameters and their degrees of observability are analyzed. Flight experiments are carried out to verify the proposed method. It is shown that the model parameters estimated by the proposed method have good accuracies, demonstrating the validity of the proposed method.

scholarly journals High-Accuracy Parameter Identification Method for Equivalent-Circuit Models of Lithium-Ion Batteries Based on the Stochastic Theory Response Reconstruction

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 834
Author(s):  
Fazheng Wen ◽  
Bin Duan ◽  
Chenghui Zhang ◽  
Rui Zhu ◽  
Yunlong Shang ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Equivalent Circuit ◽  
Stochastic Theory ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Battery Voltage ◽  
Model Parameter ◽  
Noise Interference ◽  
Model Parameter Identification ◽  
Response Reconstruction

The precision of battery modeling is usually determined by the identification of model parameters, which is dependent on the measured outside characteristic data of batteries. However, there is a lot of noise because of the environment noise and measurement error, leading to poor estimation accuracy of model parameters. This paper proposes a stochastic theory response reconstruction (STRR) method to reconstruct the measured battery voltage data, which can eliminate the noise interference and ensure high-precision model parameter identification. The relationship between the battery voltage and current is established based on the the second-order equivalent circuit model (ECM) by the convolution theorem, and the impulse function is calculated by the correlation function between the measured voltage and current. Then, the battery voltage is reconstructed and used to identify model parameters with the recursive least squares (RLS) algorithm. All data for model parameter identification is produced through the pseudo random binarysequence (PRBS) excitation signal. Finally, the Urban Dynamometer Driving Schedule (UDDS) and Federal Urban Driving Schedule (FUDS) tests are conducted to validate the performance of the proposed method. Experimental results show that when compared with the traditional solution using low-pass filter, the proposed method can eliminate the noise interference more effectively and has higher identification accuracy.

Active Excitation for Battery Model Parameter Identification

2014 ◽  
Author(s):  
Yonghua Li ◽  
Hai Yu
Keyword(s):  
Parameter Identification ◽  
Real World ◽  
Input Signal ◽  
Identification Accuracy ◽  
Model Parameters ◽  
Model Parameter ◽  
Battery Model ◽  
Battery Power ◽  
Reference Parameter ◽  
Model Parameter Identification

In this paper an active excitation approach to battery model parameter identification is discussed. Based on begin-of-life battery model, it is possible to establish a reference parameter table (either fixed, or adaptively learned), and based on such reference parameter table, as well as by analysing battery input signal, active excitation request may be generated. Active excitation is achieved based on maintaining overall torque level with regard to drive input, while adjusting both engine and battery power output (and input). Both conditions for active excitation request, as well as active excitation generation approaches, are presented in detail. Simulation examples using production electrified vehicle battery model parameters and real world drive cycles demonstrate that the proposed approach indeed improves battery model parameter identification accuracy.

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