Log in

Relevant bibliographies by topics / Parameter identification and estimation / Journal articles

To see the other types of publications on this topic, follow the link: Parameter identification and estimation.

Journal articles on the topic 'Parameter identification and estimation'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Parameter identification and estimation.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Kulczycki, Piotr. "An Algorithm for Bayes Parameter Identification." Journal of Dynamic Systems, Measurement, and Control 123, no. 4 (1999): 611–14. http://dx.doi.org/10.1115/1.1409552.

Full text

Abstract:

This paper deals with the task of parameter identification using the Bayes estimation method, which makes it possible to take into account the differing consequences of positive and negative estimation errors. The calculation procedures are based on the kernel estimators technique. The final result constitutes a complete algorithm usable for obtaining the value of the Bayes estimator on the basis of an experimentally obtained random sample. An elaborated method is provided for numerical computations.

APA, Harvard, Vancouver, ISO, and other styles

2

Kaji, Tetsuya. "Theory of Weak Identification in Semiparametric Models." Econometrica 89, no. 2 (2021): 733–63. http://dx.doi.org/10.3982/ecta16413.

Full text

Abstract:

We provide general formulation of weak identification in semiparametric models and an efficiency concept. Weak identification occurs when a parameter is weakly regular, that is, when it is locally homogeneous of degree zero. When this happens, consistent or equivariant estimation is shown to be impossible. We then show that there exists an underlying regular parameter that fully characterizes the weakly regular parameter. While this parameter is not unique, concepts of sufficiency and minimality help pin down a desirable one. If estimation of minimal sufficient underlying parameters is ineffic

APA, Harvard, Vancouver, ISO, and other styles

3

Yanuar, Ferra, Putri Trisna Sari, and Yudiantri Asdi. "IDENTIFICATION OF RAINFALL DISTRIBUTION IN WEST SUMATERA AND ASSESSMENT OF ITS PARAMETERS USING BAYES METHOD." MEDIA STATISTIKA 13, no. 2 (2020): 161–69. http://dx.doi.org/10.14710/medstat.13.2.161-169.

Full text

Abstract:

One distribution of rainfall data is a lognormal distribution with location parameters and scale parameters . This study aims to estimate the mean and variance of rainfall data in several selected cities and regencies in West Sumatra. Parameter estimation is estimated by using maximum likelihood estimation (direct method) and Bayes method. This study resulted that the Bayes method produces a better predictive value with a smaller variance value than with direct estimation. It was concluded that the estimation by the Bayes method was a better estimator method than the direct estimation.

APA, Harvard, Vancouver, ISO, and other styles

4

VOSS, HENNING U., JENS TIMMER, and JÜRGEN KURTHS. "NONLINEAR DYNAMICAL SYSTEM IDENTIFICATION FROM UNCERTAIN AND INDIRECT MEASUREMENTS." International Journal of Bifurcation and Chaos 14, no. 06 (2004): 1905–33. http://dx.doi.org/10.1142/s0218127404010345.

Full text

Abstract:

We review the problem of estimating parameters and unobserved trajectory components from noisy time series measurements of continuous nonlinear dynamical systems. It is first shown that in parameter estimation techniques that do not take the measurement errors explicitly into account, like regression approaches, noisy measurements can produce inaccurate parameter estimates. Another problem is that for chaotic systems the cost functions that have to be minimized to estimate states and parameters are so complex that common optimization routines may fail. We show that the inclusion of information

APA, Harvard, Vancouver, ISO, and other styles

5

Feng, Juqiang, Long Wu, Kaifeng Huang, Xing Zhang, and Jun Lu. "State-of-charge Estimation of Lithium-ion Battery Based Online Parameter Identification." E3S Web of Conferences 194 (2020): 02023. http://dx.doi.org/10.1051/e3sconf/202019402023.

Full text

Abstract:

Accurately estimating the state of charge (SOC) of lithium-ion is very important to improving the dynamic performance and energy utilization efficiency. In order to reduce the influence of model parameters and system coloured noise on SOC estimation accuracy, this paper proposes the SOC estimation based on online identification. Based on the mixed simplified electrochemical model, the forgetting factor recursive least squares (FFRLS) method was used to identify the parameters online, and the SOC estimation was carried out in combination with Unscented Kalman Filter (UKF). Finally, the accuracy

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Da, Lu Liu, Chuanxu Yue, Xiaojin Gao, and Yunhai Zhu. "Real-Time Estimation of the State of Charge of Lithium Batteries Under a Wide Temperature Range." Energies 18, no. 7 (2025): 1866. https://doi.org/10.3390/en18071866.

Full text

Abstract:

The state of charge (SOC) of lithium-ion batteries is essential for their proper functioning and serves as the basis for estimating other parameters within the battery management system. To enhance the accuracy of SOC estimation in lithium-ion batteries, we propose a joint estimation method that integrates lithium-ion battery parameter identification and SOC assessment using cat swarm optimization dual Kalman filtering (CSO–DKF), which accounts for variable-temperature conditions. We adopt a second-order equivalent circuit model, utilizing the Kalman filtering (KF) algorithm as a parameter fil

APA, Harvard, Vancouver, ISO, and other styles

7

Yu, Yih-Choung, J. R. Boston, Marwan Simaan, and James F. Antaki. "Identification Scheme for Cardiovascular Parameter Estimation." IFAC Proceedings Volumes 29, no. 1 (1996): 920–25. http://dx.doi.org/10.1016/s1474-6670(17)57781-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Skibinski, G. L., and W. A. Sethares. "Thermal parameter estimation using recursive identification." IEEE Transactions on Power Electronics 6, no. 2 (1991): 228–39. http://dx.doi.org/10.1109/63.76809.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Mageed Hag Elamin, Khalid Abd El. "Particle Filtering for Enhanced Parameter Estimation in Bilinear Systems Under Colored Noise." Current Research in Statistics & Mathematics 3, no. 3 (2024): 01–20. http://dx.doi.org/10.33140/crsm.03.03.01.

Full text

Abstract:

This paper addresses the challenging problem of parameter estimation in bilinear systems under colored noise. A novel approach, termed B-PF-RLS, is proposed, combining a particle filter (PF) with a recursive least squares (RLS) estimator. The B-PF-RLS algorithm tackles the complexities arising from system nonlinearities and colored noise by effectively estimating unknown system states using the particle filter, which are then integrated into the RLS parameter estimation process. Furthermore, the paper introduces an enhanced particle filter that eliminates the need for explicit knowledge of the

APA, Harvard, Vancouver, ISO, and other styles

10

Dorraki, M., M. S. Islam, A. Allison, and D. Abbott. "Parameter identification using moment of velocity." Royal Society Open Science 6, no. 11 (2019): 190671. http://dx.doi.org/10.1098/rsos.190671.

Full text

Abstract:

Many physical systems can be adequately modelled using a second-order approximation. Thus, the problem of system identification often reduces to the problem of estimating the position of a single pair of complex–conjugate poles. This paper presents a convenient but approximate technique for the estimation of the position of a single pair of complex–conjugate poles, using the moment of velocity (MoV). The MoV is a Hilbert transform based signal processing tool that addresses the shortcomings of instantaneous frequency. We demonstrate that the MoV can be employed for parameter identification of

APA, Harvard, Vancouver, ISO, and other styles

11

Krauße, T., and J. Cullmann. "Identification of hydrological model parameters for flood forecasting using data depth measures." Hydrology and Earth System Sciences Discussions 8, no. 2 (2011): 2423–76. http://dx.doi.org/10.5194/hessd-8-2423-2011.

Full text

Abstract:

Abstract. The development of methods for estimating the parameters of hydrological models considering uncertainties has been of high interest in hydrological research over the last years. Besides the very popular Markov Chain Monte Carlo (MCMC) methods which estimate the uncertainty of model parameters in the settings of a Bayesian framework, the development of depth based sampling methods, also entitled robust parameter estimation (ROPE), have attracted an increasing research interest. These methods understand the estimation of model parameters as a geometric search of a set of robust perform

APA, Harvard, Vancouver, ISO, and other styles

12

Mamatov, Aleksandr, Sergey Lovlin, Toomas Vaimann, Anton Rassõlkin, Sergei Vakulenko, and Andrei Abramian. "Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise." Electronics 8, no. 10 (2019): 1200. http://dx.doi.org/10.3390/electronics8101200.

Full text

Abstract:

The paper considers the problem of parameter identification of the surface mounted permanent magnet synchronous motor (SPMSM) with pulse width modulated (PWM) inverter in the presence of dead time of power switches and other nonlinear distortions. Parameter identification of the SPMSM is required for the tuning of the torque control loop, because in some cases, the exact values of phase resistances and inductances are not known. In the absence of nonlinear disturbances, the problem of SPMSM parameters estimation is not difficult. The influence of the dead-time effect, back electromotive force

APA, Harvard, Vancouver, ISO, and other styles

13

Olarte Dussán, Fredy Andrés, Carlos Eduardo Borda Zapata, and Hernando Díaz Morales. "State Estimation-based Transmission line parameter identification." Ingeniería e Investigación 30, no. 1 (2010): 56–63. http://dx.doi.org/10.15446/ing.investig.v30n1.15208.

Full text

Abstract:

This article presents two state-estimation-based algorithms for identifying transmission line parameters. The identification technique used simultaneous state-parameter estimation on an artificial power system composed of several copies of the same transmission line, using measurements at different points in time. The first algorithm used active and reactive power measurements at both ends of the line. The second method used synchronised phasor voltage and current measurements at both ends. The algorithms were tested in simulated conditions on the 30-node IEEE test system. All line parameters

APA, Harvard, Vancouver, ISO, and other styles

14

Zhang, Xiao, Feng Ding, Ling Xu, Ahmed Alsaedi, and Tasawar Hayat. "A Hierarchical Approach for Joint Parameter and State Estimation of a Bilinear System with Autoregressive Noise." Mathematics 7, no. 4 (2019): 356. http://dx.doi.org/10.3390/math7040356.

Full text

Abstract:

This paper is concerned with the joint state and parameter estimation methods for a bilinear system in the state space form, which is disturbed by additive noise. In order to overcome the difficulty that the model contains the product term of the system input and states, we make use of the hierarchical identification principle to present new methods for estimating the system parameters and states interactively. The unknown states are first estimated via a bilinear state estimator on the basis of the Kalman filtering algorithm. Then, a state estimator-based recursive generalized least squares (

APA, Harvard, Vancouver, ISO, and other styles

15

Lian, Xie, Xiaolong Hu, Liangsheng Shi, Jinhua Shao, Jiang Bian, and Yuanlai Cui. "Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning." Water 16, no. 6 (2024): 896. http://dx.doi.org/10.3390/w16060896.

Full text

Abstract:

The parameters of the GR4J-CemaNeige coupling model (GR4neige) are typically treated as constants. However, the maximum capacity of the production store (parX1) exhibits time-varying characteristics due to climate variability and vegetation coverage change. This study employed differentiable parameter learning (dPL) to identify the time-varying parX1 in the GR4neige across 671 catchments within the United States. We built two types of dPL, including static and dynamic parameter networks, to assess the advantages of the time-varying parameter. In the dynamic parameter network, we evaluated the

APA, Harvard, Vancouver, ISO, and other styles

16

Xia, Bizhong, Rui Huang, Zizhou Lao, et al. "Online Parameter Identification of Lithium-Ion Batteries Using a Novel Multiple Forgetting Factor Recursive Least Square Algorithm." Energies 11, no. 11 (2018): 3180. http://dx.doi.org/10.3390/en11113180.

Full text

Abstract:

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 t

APA, Harvard, Vancouver, ISO, and other styles

17

Liu, Fang, Jie Ma, Weixing Su, Hanning Chen, and Maowei He. "Research on Parameter Self-Learning Unscented Kalman Filtering Algorithm and Its Application in Battery Charge of State Estimation." Energies 13, no. 7 (2020): 1679. http://dx.doi.org/10.3390/en13071679.

Full text

Abstract:

A novel state estimation algorithm based on the parameters of a self-learning unscented Kalman filter (UKF) with a model parameter identification method based on a collaborative optimization mechanism is proposed in this paper. This algorithm can realize the dynamic self-learning and self-adjustment of the parameters in the UKF algorithm and the automatic optimization setting Sigma points without human participation. In addition, the multi-algorithm collaborative optimization mechanism unifies a variety of algorithms, so that the identification method has the advantages of member algorithms wh

APA, Harvard, Vancouver, ISO, and other styles

18

韦, 超毅. "Parameter Identification of Li-ion Battery Based on Parameter Estimation Toolbox." Advances in Energy and Power Engineering 09, no. 03 (2021): 132–39. http://dx.doi.org/10.12677/aepe.2021.93014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Ma, Changlong, Chao Wu, Luoya Wang, et al. "A Review of Parameter Identification and State of Power Estimation Methods for Lithium-Ion Batteries." Processes 12, no. 10 (2024): 2166. http://dx.doi.org/10.3390/pr12102166.

Full text

Abstract:

Lithium-ion batteries are widely applied in the form of new energy electric vehicles and large-scale battery energy storage systems to improve the cleanliness and greenness of energy supply systems. Accurately estimating the state of power (SOP) of lithium-ion batteries ensures long-term, efficient, safe and reliable battery operation. Considering the influence of the parameter identification accuracy on the results of state of power estimation, this paper presents a systematic review of model parameter identification and state of power estimation methods for lithium-ion batteries. The paramet

APA, Harvard, Vancouver, ISO, and other styles

20

Juan Carlos Gómez and Gonzalo Daniel Sad. "OPTIMAL RECURSIVE IDENTIFICATION TECHNIQUES FROM QUANTIZED OUTPUTS." Latin American Applied Research - An international journal 53, no. 4 (2023): 423–28. http://dx.doi.org/10.52292/j.laar.2023.3279.

Full text

Abstract:

Recursive identification algorithms for the parameter estimation of linear systems from multilevel quantized outputs are introduced in this paper. The proposed algorithms are proved to be optimal in the sense that they minimize the a posteriori parameter estimation error covariance matrix. Numerical simulations are carried out to illustrate their performance for different quantization steps and Signal-to-Noise Ratios, as well as their capability to track time varying parameters.

APA, Harvard, Vancouver, ISO, and other styles

21

Liu, Xiaofeng, Bangzhao Zhou, Boyang Xiao, and Guoping Cai. "Inertia parameter identification of anunknown captured space target." Aircraft Engineering and Aerospace Technology 91, no. 8 (2019): 1147–55. http://dx.doi.org/10.1108/aeat-04-2018-0128.

Full text

Abstract:

Purpose The purpose of this paper is to present a method to obtain the inertia parameter of a captured unknown space target. Design/methodology/approach An inertia parameter identification method is proposed in the post-capture scenario in this paper. This method is to resolve parameter identification with two steps: coarse estimation and precise estimation. In the coarse estimation step, all the robot arms are fixed and inertia tensor of the combined system is first calculated by the angular momentum conservation equation of the system. Then, inertia parameters of the unknown target are estim

APA, Harvard, Vancouver, ISO, and other styles

22

Santos Neto, Accacio Ferreira dos, Murillo Ferreira dos Santos, Mathaus Ferreira da Silva, Leonardo de Mello Honório, Edimar José de Oliveira, and Edvaldo Soares Araújo Neto. "Performance Comparison of Meta-Heuristics Applied to Optimal Signal Design for Parameter Identification." Sensors 23, no. 22 (2023): 9085. http://dx.doi.org/10.3390/s23229085.

Full text

Abstract:

This paper presents a comparative study that explores the performance of various meta-heuristics employed for Optimal Signal Design, specifically focusing on estimating parameters in nonlinear systems. The study introduces the Robust Sub-Optimal Excitation Signal Generation and Optimal Parameter Estimation (rSOESGOPE) methodology, which is originally derived from the well-known Particle Swarm Optimization (PSO) algorithm. Through a real-life case study involving an Autonomous Surface Vessel (ASV) equipped with three Degrees of Freedom (DoFs) and an aerial holonomic propulsion system, the effec

APA, Harvard, Vancouver, ISO, and other styles

23

Campbell, Mark E. "Identification and Parameter Estimation for Control Design." IFAC Proceedings Volumes 29, no. 1 (1996): 4138–43. http://dx.doi.org/10.1016/s1474-6670(17)58329-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Liu, W. H. E., and Swee-Lian Lim. "Parameter error identification and estimation in power system state estimation." IEEE Transactions on Power Systems 10, no. 1 (1995): 200–209. http://dx.doi.org/10.1109/59.373943.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Fradkov, Alexander L., Aleksandr Kovalchukov, and Boris Andrievsky. "Parameter Estimation for Hindmarsh–Rose Neurons." Electronics 11, no. 6 (2022): 885. http://dx.doi.org/10.3390/electronics11060885.

Full text

Abstract:

In the paper, a new adaptive model of a neuron based on the Hindmarsh–Rose third-order model of a single neuron is proposed. The learning algorithm for adaptive identification of the neuron parameters is proposed and analyzed both theoretically and by computer simulation. The proposed algorithm is based on the Lyapunov functions approach and reduced adaptive observer. It allows one to estimate parameters of the population of the neurons if they are synchronized. The rigorous stability conditions for synchronization and identification are presented.

APA, Harvard, Vancouver, ISO, and other styles

26

Ji, Yan, and Jinde Cao. "Parameter Estimation Algorithms for Hammerstein Finite Impulse Response Moving Average Systems Using the Data Filtering Theory." Mathematics 10, no. 3 (2022): 438. http://dx.doi.org/10.3390/math10030438.

Full text

Abstract:

This paper considers the parameter estimation problems of Hammerstein finite impulse response moving average (FIR–MA) systems. Based on the matrix transformation and the hierarchical identification principle, the Hammerstein FIR–MA system is recast into two models, and a decomposition-based recursive least-squares algorithm is deduced for estimating the parameters of these two models. In order to further improve the accuracy of the parameter estimation, a multi-innovation hierarchical least-squares algorithm based on the data filtering theory proposed. Finally, a simulation example demonstrate

APA, Harvard, Vancouver, ISO, and other styles

27

Zhou, Si-Da, Li Liu, Wu Yang, and Zhi-Sai Ma. "Operational Modal Identification of Time-Varying Structures via a Vector Multistage Recursive Approach in Hybrid Time and Frequency Domain." Shock and Vibration 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/397364.

Full text

Abstract:

Real-time estimation of modal parameters of time-varying structures can conduct an obvious contribution to some specific applications in structural dynamic area, such as health monitoring, damage detection, and vibration control; the recursive algorithm of modal parameter estimation supplies one of fundamentals for acquiring modal parameters in real-time. This paper presents a vector multistage recursive method of modal parameter estimation for time-varying structures in hybrid time and frequency domain, including stages of recursive estimation of time-dependent power spectra, frozen-time moda

APA, Harvard, Vancouver, ISO, and other styles

28

Wang, Wenjie, Lingtao Yu, and Jing Yang. "Linear parameter variant modeling and parameter identification of a cable-driven micromanipulator for surgical robot." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 233, no. 5 (2018): 1828–40. http://dx.doi.org/10.1177/0954406218773780.

Full text

Abstract:

This paper proposes a novel cable-driven micromanipulator for surgical robots. A single-joint principle prototype for surgical robot micromanipulator was manufactured to test the proposed design. Elasticity and friction were assessed to establish a joint angle estimator; estimator parameters were obtained by a combination of least square method and genetic algorithm. Angle closed-loop control was performed by considering the joint angle estimator output as the feedback signal. A nonlinear dynamic model was established in the state-space and described as a linear parameter variant model. The dy

APA, Harvard, Vancouver, ISO, and other styles

29

Qin, Taichun, Hua Shao, Yuege Zhou, and Guangming Zhao. "Applicability analysis of equivalent circuit model parameter identification method for lithium-ion batteries." Journal of Physics: Conference Series 2993, no. 1 (2025): 012077. https://doi.org/10.1088/1742-6596/2993/1/012077.

Full text

Abstract:

Abstract The equivalent circuit model (ECM) of lithium-ion batteries is commonly utilized for state of charge (SOC) online estimation. In recent years, researchers have proposed different ECM identification methods according to their own needs, so the application scenarios of each method are different. The ECM identification consists of two steps: (1) obtaining the open circuit voltage curve and (2) obtaining the parameters of ECM. Since the OCV curve acquisition process is common, the difference between these estimation methods lies in the different parameter estimation methods. Since high dy

APA, Harvard, Vancouver, ISO, and other styles

30

Coll, Carmen, and Elena Sánchez. "Parameter Identification and Estimation For Stage–Structured Population Models." International Journal of Applied Mathematics and Computer Science 29, no. 2 (2019): 327–36. http://dx.doi.org/10.2478/amcs-2019-0024.

Full text

Abstract:

Abstract A stage-structured population model with unknown parameters is considered. Our purpose is to study the identifiability of the model and to develop a parameter estimation procedure. First, we analyze whether the parameter vector can or cannot uniquely be determined with the knowledge of the input-output behavior of the model. Second, we analyze how the information in the experimental data is translated into the parameters of the model. Furthermore, we propose a process to improve the recursive values of the parameters when successive observation data are considered. The structure of th

APA, Harvard, Vancouver, ISO, and other styles

31

Zhang, Shuo, Dongqing Wang, and Feng Liu. "Separate block-based parameter estimation method for Hammerstein systems." Royal Society Open Science 5, no. 6 (2018): 172194. http://dx.doi.org/10.1098/rsos.172194.

Full text

Abstract:

Different from the output–input representation-based identification methods of two-block Hammerstein systems, this paper concerns a separate block-based parameter estimation method for each block of a two-block Hammerstein CARMA system, without combining the parameters of two parts together. The idea is to consider each block as a subsystem and to estimate the parameters of the nonlinear block and the linear block separately (interactively), by using two least-squares algorithms in one recursive step. The internal variable between the two blocks (the output of the nonlinear block, and also the

APA, Harvard, Vancouver, ISO, and other styles

32

Li, Zhengbin, Lijun Ma, and Yongqiang Wang. "Parameter estimation for nonlinear sandwich system using instantaneous performance principle." PLOS ONE 17, no. 12 (2022): e0271160. http://dx.doi.org/10.1371/journal.pone.0271160.

Full text

Abstract:

The vast majority of reports mainly focus on the steady-state performance of parameter estimation. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. In this study, the parameter estimation of

APA, Harvard, Vancouver, ISO, and other styles

33

Al-Mashhadani, Mohammad Abdulrahman. "Optimal control and state estimation for unmanned aerial vehicle under random vibration and uncertainty." Measurement and Control 52, no. 9-10 (2019): 1264–71. http://dx.doi.org/10.1177/0020294019866860.

Full text

Abstract:

In the past decade, many approaches that attempted to solve the problem of optimal control and parameter estimation of an unmanned aerial vehicle with a priori uncertain parameters simply implied two ways to solve such problem. First, by the formation of optimal control based on a refined mathematical model of the unmanned aerial vehicle, and second, by using the estimation and identification methods of the model parameter of the unmanned aerial vehicle based on measured data from flight tests. However, the identification of the dynamic parameters of the unmanned aerial vehicle is a complicate

APA, Harvard, Vancouver, ISO, and other styles

34

Wang, Jianlin, Le Zhang, Dan Xu, Peng Zhang, and Gairu Zhang. "A Simplified Fractional Order Equivalent Circuit Model and Adaptive Online Parameter Identification Method for Lithium-Ion Batteries." Mathematical Problems in Engineering 2019 (April 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/6019236.

Full text

Abstract:

In order to improve the battery management system performance and enhance the adaptability of the system, a fractional order equivalent circuit model of lithium-ion battery based on electrochemical test was established. The parameters of the fractional order equivalent circuit model are identified by the least squares parameter identification method. The least squares parameter identification method needs to rely on the harsh test conditions of the laboratory, and the parameter identification result is static; it cannot adapt to the characteristics of the lithium battery under dynamic conditio

APA, Harvard, Vancouver, ISO, and other styles

35

Li, Haifeng, Qing Chen, Chang Fu, Zhe Yu, Di Shi, and Zhiwei Wang. "Bayesian Estimation on Load Model Coefficients of ZIP and Induction Motor Model." Energies 12, no. 3 (2019): 547. http://dx.doi.org/10.3390/en12030547.

Full text

Abstract:

Parameter identification in load models is a critical factor for power system computation, simulation, and prediction, as well as stability and reliability analysis. Conventional point estimation based composite load modeling approaches suffer from disturbances and noises, and provide limited information of the system dynamics. In this work, a statistics (Bayesian Estimation) based distribution estimation approach is proposed for both static and dynamic load models. When dealing with multiple parameters, Gibbs sampling method is employed. The proposed method samples all parameters in each iter

APA, Harvard, Vancouver, ISO, and other styles

36

Fan, Ying, Shun Kun Wang, Feng Zhou, Zhi Cheng Tian, and Guang Shuai Ding. "Parameter Estimation for Small Sample Censored Data Based on SVM." Advanced Materials Research 145 (October 2010): 31–36. http://dx.doi.org/10.4028/www.scientific.net/amr.145.31.

Full text

Abstract:

It is difficult to identify distribution types and to estimate parameters of the distribution for small sample censored data when you deal with mechanical equipment reliability analysis. Here, an intelligent distribution identification model was established based on statistical learning theory and the algorithm of multi-element classifier of Support Vector Machine (SVM), and also applied to parameter estimation of small sample censored data, in order to improve the precision of traditional method. Firstly, the algorithm of training based on SVM and the RBF kernel function was selected; secondl

APA, Harvard, Vancouver, ISO, and other styles

37

Lakshminarayana, Subhash, Saurav Sthapit, and Carsten Maple. "Application of Physics-Informed Machine Learning Techniques for Power Grid Parameter Estimation." Sustainability 14, no. 4 (2022): 2051. http://dx.doi.org/10.3390/su14042051.

Full text

Abstract:

Power grid parameter estimation involves the estimation of unknown parameters, such as the inertia and damping coefficients, from the observed dynamics. In this work, we present physics-informed machine learning algorithms for the power system parameter estimation problem. First, we propose a novel algorithm to solve the parameter estimation based on the Sparse Identification of Nonlinear Dynamics (SINDy) approach, which uses sparse regression to infer the parameters that best describe the observed data. We then compare its performance against another benchmark algorithm, namely, the physics-i

APA, Harvard, Vancouver, ISO, and other styles

38

Awoda, Murtadha, and Ramzy Ali. "Parameter Estimation of a Permanent Magnetic DC Motor." Iraqi Journal for Electrical and Electronic Engineering 15, no. 1 (2019): 28–36. http://dx.doi.org/10.37917/ijeee.15.1.3.

Full text

Abstract:

The identification of system parameters plays an essential role in system modeling and control. This paper presents a parameter estimation for a permanent magnetic DC motor using the simulink design optimization method. The parameter estimation may be represented as an optimization problem. Firstly, the initial values of the DC motor parameters are extracted using the dynamic model through measuring the values of voltage, current, and speed of the motor. Then, these values are used as an initial value for simulink design optimization. The experimentally inputoutput data can be collected using

APA, Harvard, Vancouver, ISO, and other styles

39

Li, Yunxia, and Lei Li. "Parameter Estimation of a Countershaft Brake for Heavy-Duty Vehicles with Automated Mechanical Transmission." Processes 9, no. 6 (2021): 1036. http://dx.doi.org/10.3390/pr9061036.

Full text

Abstract:

A countershaft brake is used as a transmission brake (TB) to realize synchronous shifting by reducing the automated mechanical transmission (AMT) input shaft’s speed rapidly. This process is performed to reduce shifting time and improve shifting quality for heavy-duty vehicles equipped with AMT without synchronizer. To improve controlled synchronous shifting, the AMT input shaft’s equivalent resistance torque and the TB’s characteristic parameters are studied. An AMT dynamic model under neutral gear position is analyzed during the synchronous control interval. A dynamic model of the countersha

APA, Harvard, Vancouver, ISO, and other styles

40

Avcıoğlu, Sevil, Ali Türker Kutay, and Kemal Leblebicioğlu. "Identification of Physical Helicopter Models Using Subspace Identification." Journal of the American Helicopter Society 65, no. 2 (2020): 1–14. http://dx.doi.org/10.4050/jahs.65.022001.

Full text

Abstract:

Subspace identification is a powerful tool due to its well-understood techniques based on linear algebra (orthogonal projections and intersections of subspaces) and numerical methods like singular value decomposition. However, the state space model matrices, which are obtained from conventional subspace identification algorithms, are not necessarily associated with the physical states. This can be an important deficiency when physical parameter estimation is essential. This holds for the area of helicopter flight dynamics, where physical parameter estimation is mainly conducted for mathematica

APA, Harvard, Vancouver, ISO, and other styles

41

Han, Sukjin, and Adam McCloskey. "Estimation and inference with a (nearly) singular Jacobian." Quantitative Economics 10, no. 3 (2019): 1019–68. http://dx.doi.org/10.3982/qe989.

Full text

Abstract:

This paper develops extremum estimation and inference results for nonlinear models with very general forms of potential identification failure when the source of this identification failure is known. We examine models that may have a general deficient rank Jacobian in certain parts of the parameter space. When identification fails in one of these models, it becomes underidentified and the identification status of individual parameters is not generally straightforward to characterize. We provide a systematic reparameterization procedure that leads to a reparametrized model with straightforward

APA, Harvard, Vancouver, ISO, and other styles

42

Li, Pu, and Quoc Dong Vu. "Identification of parameter correlations for parameter estimation in dynamic biological models." BMC Systems Biology 7, no. 1 (2013): 91. http://dx.doi.org/10.1186/1752-0509-7-91.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Rudenko, O. G., О. О. Bessonov, N. М. Serdyuk, К. О. Olijnik, and О. S. Romanyuk. "Robust object identification in the presence of non-Gaussian interference." Bionics of Intelligence 2, no. 93 (2019): 7–12. http://dx.doi.org/10.30837/bi.2019.2(93).02.

Full text

Abstract:

The problem of identifying the parameters of a linear object in the presence of non-Gaussian interference is considered based on minimizing a combined functional that combines the properties of OLS and IIS. The conditions for the convergence of the gradient identification algorithm in mean and mean square are determined. Analytical estimates are obtained for non-asymptotic and asymptotic values of the parameter estimation error and the identification accuracy. It is shown that these values of the estimation error and identification accuracy depend on the choice of the mixing parameter.

APA, Harvard, Vancouver, ISO, and other styles

44

Wu, Qize, Chenyu Wang, Jiayi Zhao, et al. "Research on Parameter Identification of Induction Motor Based on Model Reference Adaptive System." Journal of Physics: Conference Series 2785, no. 1 (2024): 012059. http://dx.doi.org/10.1088/1742-6596/2785/1/012059.

Full text

Abstract:

Abstract This paper proposes a novel online parameter identification method based on model-reference adaptive systems to achieve more precise control of induction motors. This method efficiently and accurately obtains the parameters of induction motors during operation, facilitating effective control. Experimental validation is conducted on a hardware-in-the-loop test platform, demonstrating that the proposed online parameter identification method for induction motors achieves accurate parameter estimation with the advantages of high identification speed and precision. Control experiments on i

APA, Harvard, Vancouver, ISO, and other styles

45

Schartau, Markus, Philip Wallhead, John Hemmings, et al. "Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling." Biogeosciences 14, no. 6 (2017): 1647–701. http://dx.doi.org/10.5194/bg-14-1647-2017.

Full text

Abstract:

Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explai

APA, Harvard, Vancouver, ISO, and other styles

46

Fan, Huai Ke, and Wei Xing Lin. "Parameter Estimation of the MISO Nonlinear System Based on Improved Particle Swarm Optimization." Applied Mechanics and Materials 130-134 (October 2011): 2563–67. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2563.

Full text

Abstract:

Nonlinear system identification is a main topic of modern identification. This paper presents a new parameter estimation method of MISO (multiple inputs, single output) Hammerstein model by using improved particle swarm optimization (IPSO). The basic idea of the method is that the model identification problem is converted into optimization of nonlinear function over parameter space. And the swarm intelligence method is used to search the parameter space concurrently and efficiently in order to find the optimal estimation of the model parameter. The basic algorithms of IPSO and the parameter co

APA, Harvard, Vancouver, ISO, and other styles

47

HANAZAKI, Izumi, Tatsuro KOIKE, and Kageo AKIZUKI. "Numerical Accuracy of Parameter Estimation in System Identification." Transactions of the Society of Instrument and Control Engineers 22, no. 10 (1986): 1043–50. http://dx.doi.org/10.9746/sicetr1965.22.1043.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Gabano, Jean-Denis, and Thierry Poinot. "Fractional identification algorithms applied to thermal parameter estimation." IFAC Proceedings Volumes 42, no. 10 (2009): 1316–21. http://dx.doi.org/10.3182/20090706-3-fr-2004.00219.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Jakubek, S., and C. Hametner. "Identification of Neurofuzzy Models Using GTLS Parameter Estimation." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, no. 5 (2009): 1121–33. http://dx.doi.org/10.1109/tsmcb.2009.2013132.

Full text

APA, Harvard, Vancouver, ISO, and other styles

50

Orman, Maciej, Michal Orkisz, and Cajetan T. Pinto. "Parameter identification and slip estimation of induction machine." Mechanical Systems and Signal Processing 25, no. 4 (2011): 1408–16. http://dx.doi.org/10.1016/j.ymssp.2010.11.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!