Hybrid GA-ACO Algorithm for a Model Parameters Identification Problem

This paper introduces an approach for parameters identification of a statistical predicting model with the use of the available individual data. Unknown parameters are separated into two groups: the ones specifying the average trend over large set of individuals and the ones describing the details of a concrete person. In order to calculate the vector of unknown parameters, a multidimensional constrained optimization problem is solved minimizing the discrepancy between real data and the model prediction over the set of feasible solutions. Both the individual retrospective data and factors influencing the individual dynamics are taken into account. The application of the method for predicting the movement of a patient with congenital motility disorders is considered.

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Effective model parameters identification for open quantum systems

2011 International Conference on Electrical and Control Engineering ◽

10.1109/iceceng.2011.6057456 ◽

2011 ◽

Cited By ~ 1

Author(s):

Erling Gong ◽

Weiwei Zhou ◽

Ming Zhang ◽

S. G. Schirmer

Keyword(s):

Open Quantum Systems ◽

Quantum Systems ◽

Parameters Identification ◽

Effective Model ◽

Model Parameters ◽

Open Quantum

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Online State of Charge Estimation for Lithium-Ion Battery by Combining Incremental Autoregressive and Moving Average Modeling with Adaptive H-Infinity Filter

Mathematical Problems in Engineering ◽

10.1155/2018/7480602 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Zheng Liu ◽

Xuanju Dang ◽

Hanxu Sun

Keyword(s):

Moving Average ◽

Equivalent Circuit Model ◽

Lithium Ion ◽

State Of Charge ◽

Parameters Identification ◽

Estimation Methods ◽

Model Parameters ◽

Battery Management System ◽

H Infinity ◽

Soc Estimation

The state of charge (SOC) estimation is one of the most important features in battery management system (BMS) for electric vehicles (EVs). In this article, a novel equivalent-circuit model (ECM) with an extra noise sequence is proposed to reduce the adverse effect of model error. Model parameters identification method with variable forgetting factor recursive extended least squares (VFFRELS), which combines a constructed incremental autoregressive and moving average (IARMA) model with differential measurement variables, is presented to obtain the ECM parameters. The independent open circuit voltage (OCV) estimator with error compensation factors is designed to reduce the OCV error of OCV fitting model. Based on the IARMA battery model analysis and the parameters identification, an SOC estimator by adaptive H-infinity filter (AHIF) is formulated. The adaptive strategy of the AHIF improves the numerical stability and robust performance by synchronous adjusting noise covariance and restricted factor. The results of experiment and simulation have verified that the proposed approach has superior advantage of parameters identification and SOC estimation to other estimation methods.

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Parameter Identification for Nonlinear Dry-Friction Structure of Metallic Rubber

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.52-54.494 ◽

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.

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The inverse jiles-atherton model parameters identification

IEEE Transactions on Magnetics ◽

10.1109/tmag.2003.810216 ◽

2003 ◽

Vol 39 (3) ◽

pp. 1397-1400 ◽

Cited By ~ 36

Author(s):

J.V. Leite ◽

N. Sadowski ◽

P. Kuo-Peng ◽

N.J. Batistela ◽

J.P.A. Bastos

Keyword(s):

Parameters Identification ◽

Model Parameters

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