Parameter estimation method for coupled tank system using Dual Extended Kalman filter

2013 ◽  
Author(s):  
Ji-Hoon Seung ◽  
Deok-Jin Lee ◽  
Kil-To Chong
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Method ◽  
Parameter Estimation Method ◽  
Tank System

scholarly journals Auto-SOM: Recursive Parameter Estimation for Guidance of Self-Organizing Feature Maps

2001 ◽  
Vol 13 (3) ◽  
pp. 595-619 ◽  
Author(s):  
Karin Haese ◽  
Geoffrey J. Goodhill
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Estimation Error ◽  
Estimation Method ◽  
Original Form ◽  
Self Organizing Map ◽  
Feature Maps ◽  
Parameter Estimation Method ◽  
Recursive Parameter Estimation ◽  
Self Organizing

An important technique for exploratory data analysis is to form a mapping from the high-dimensional data space to a low-dimensional representation space such that neighborhoods are preserved. A popular method for achieving this is Kohonen's self-organizing map (SOM) algorithm. However, in its original form, this requires the user to choose the values of several parameters heuristically to achieve good performance. Here we present the Auto-SOM, an algorithm that estimates the learning parameters during the training of SOMs automatically. The application of Auto-SOM provides the facility to avoid neighborhood violations up to a user-defined degree in either mapping direction. Auto-SOM consists of a Kalman filter implementation of the SOM coupled with a recursive parameter estimation method. The Kalman filter trains the neurons' weights with estimated learning coefficients so as to minimize the variance of the estimation error. The recursive parameter estimation method estimates the width of the neighborhood function by minimizing the prediction error variance of the Kalman filter. In addition, the “topographic function” is incorporated to measure neighborhood violations and prevent the map's converging to configurations with neighborhood violations. It is demonstrated that neighborhoods can be preserved in both mapping directions as desired for dimension-reducing applications. The development of neighborhood-preserving maps and their convergence behavior is demonstrated by three examples accounting for the basic applications of self-organizing feature maps.

scholarly journals A Parameter-Estimation Method Using the Ensemble Kalman Filter for Flow and Thermal Simulation in an Engine Compartment

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Kazuya Kusano ◽  
Hironobu Yamakawa ◽  
Kenich Hano
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Measurement Data ◽  
Estimation Method ◽  
Observation Error ◽  
Model Errors ◽  
Engine Compartment ◽  
Actual Measurement ◽  
Parameter Estimation Method

The feasibility of the parameter estimation on the basis of the ensemble Kalman filter (EnKF) for a practical simulation involving model errors was investigated. The three-dimensional flow and thermal simulations for the engine compartment of a test excavator were simulated, and several unknown temperatures used for boundary conditions were estimated with the method. The estimation method was validated in two steps. First, the estimation method was tested with the influence of the model errors removed by virtually creating true values with a simulation. These results showed that the proposed parameter-estimation method can successfully estimate surface temperatures. They also suggested that the appropriate ensemble size can be evaluated from the number of unknown parameters. Second, the estimation method was tested under a practical condition including model errors by using actual measurement data. Model errors were statistically estimated using prior obtained error data concerning other design configurations, and they were added to the observation error in the EnKF. These results showed that taking model errors into account in the EnKF provides more-accurate parameter-estimation results. Moreover, the uncertainty of an estimated parameter can be evaluated with the standard deviation of its distribution.

scholarly journals Modeling and Fault Diagnosis of Interturn Short Circuit for Five-Phase Permanent Magnet Synchronous Motor

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jian-wei Yang ◽  
Man-feng Dou ◽  
Zhi-yong Dai
Keyword(s):  
Parameter Estimation ◽  
Fault Diagnosis ◽  
Permanent Magnet ◽  
Fault Tolerant ◽  
Short Circuit ◽  
Estimation Method ◽  
Trust Region ◽  
Fault Tolerant Control ◽  
Trust Region Algorithm ◽  
Parameter Estimation Method

Taking advantage of the high reliability, multiphase permanent magnet synchronous motors (PMSMs), such as five-phase PMSM and six-phase PMSM, are widely used in fault-tolerant control applications. And one of the important fault-tolerant control problems is fault diagnosis. In most existing literatures, the fault diagnosis problem focuses on the three-phase PMSM. In this paper, compared to the most existing fault diagnosis approaches, a fault diagnosis method for Interturn short circuit (ITSC) fault of five-phase PMSM based on the trust region algorithm is presented. This paper has two contributions. (1) Analyzing the physical parameters of the motor, such as resistances and inductances, a novel mathematic model for ITSC fault of five-phase PMSM is established. (2) Introducing an object function related to the Interturn short circuit ratio, the fault parameters identification problem is reformulated as the extreme seeking problem. A trust region algorithm based parameter estimation method is proposed for tracking the actual Interturn short circuit ratio. The simulation and experimental results have validated the effectiveness of the proposed parameter estimation method.

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