• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.5
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• pp.838-847
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• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.5 s.176
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• pp.1343-1351
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• 2000

Thermally induced errors have been significant factors affecting the machine tool accuracy. In this paper, the spindle thermal error has been focused, where the 5 degree of freedom thermal error components are considered. An effective measurement system has been devised for the 5 DOF thermal errors, consisting of gap sensors and thermocouples around the micro-computer interfaced environment. Several thermal error modeling techniques are also implemented for the thermal error prediction: multiple linear regression, neural network and system identification methods, etc. The performance of the thermal error modeling techniques is evaluated and compared, giving the system identification method as the optimum model having the least deviation. The developed system for the thermal error measurement and modeling was practically applied to a CNC machining center, and the spindle thermal errors were effectively compensated around the micro computer-machine tool interfaced networks. The machine tool accuracy was improved about 4-5 times typically.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.

• The Transactions of the Korean Institute of Power Electronics
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• v.25 no.3
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• pp.227-234
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• 2020

This paper presents the methods for the modeling and parameter optimization of the electric vehicle battery. The state variables of the battery are defined, and the test methods for battery parameters are presented. The state-space equation, which consists of four state variables, and the output equation, which is a combination of to-be-determined parameters, are shown. The parameter optimization method is the key point of this study. The least square of the modeling error can be used as an initial value of the multivariable function. It is equivalent to find the minimum value of the error function to obtain optimal parameters from multivariable function. The SIMULINK model is presented, and the 10-hour full operational range test results are shown to verify the performance of the model. The modeling error for 25 degrees is approximately 1% for full operational ranges. The comments to enhance modeling accuracy are shown in the conclusion.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.71-72
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• 2008

• Structural Engineering and Mechanics
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• v.8 no.5
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• pp.513-529
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• 1999

A concept of hierarchical modeling, the newest modeling technology, has been introduced in early 1990's. This new technology has a great potential to advance the capabilities of current computational mechanics. A first step to implement this concept is to construct hierarchical models, a family of mathematical models sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics in their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-, plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical, analysis of hierarchical models, two kinds of errors prevail, the modeling error and the numerical approximation error. To ensure numerical simulation quality, an accurate estimation of these two errors is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures is derived using the element residuals and the flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error indicators for two types of errors, in the energy norm. Compared to the classical error estimators using the flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.8
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• pp.566-573
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• 2019

In order to obtain high-quality images by motion compensation in the airborne synthetic aperture radar (SAR), accurate motion sensing in image acquisition section is necessary. Especially, reducing relative position error and discontinuity in motion sensing is important. To overcome the problem, we propose a pre-parametric error modeling (P-PEM) algorithm which is a real-time motion sensing algorithm for the airborne SAR in this paper. P-PEM is an extended version of parametric error modeling (PEM) method which is a motion sensing algorithm to mitigate the errors in the previous work. PEM estimates polynomial coefficients of INS error which can be assumed as a polynomial in the short term. Otherwise, P-PEM estimates polynomial coefficients in advance and uses at image acquisition section. Simulation results show that the P-PEM reduces relative position error and discontinuity effectively in real-time.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.54-61
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• 2008

This paper presents a method for reducing modelling error in the linear motion system with voicecoil actuator (VCA). A model of linear motion system composed of a mechanism and control was prepared to verify the proposed method. In modeling of the system, the damping coefficient obtained experimentally is applied to the model in order to consider the effect of the viscous friction for the moving part in VCA. The response velocity of VCA for duty ratio of PWM signal was analyzed in the time domain. Consequently, the relation between velocity and duty ratio was obtained. The result from the experiment showed an error of 9% when compared with that of simulation. In order to reduce the modeling error, impedance variation according to input frequency was analyzed, and equivalent impedance with multi-frequency was applied to the control part. As a result, the modeling error decreased to 5%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.10
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• pp.833-839
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• 2012

In this paper, we analyze the reconstruction error in the modeling of flow field on BFS(Backward Facing Step). In order for the mathematical modelling of a density on the field, the spatial and temporal modes are extracted by POD(Proper Orthogonal Decomposition) method. After formulating the modeling error, we summarize the relationship between the energy strength and the reconstruction errors. Moreover the allowable modeling error limits in the flow control point of view are confined by analysing in the frequency domain as well as time domain of the reconstructed data.

• Journal of Theoretical and Applied Mechanics
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• v.3 no.1
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• pp.16-33
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• 2002

A concept of hierarchical modeling, the newest modeling technology. has been introduced early In 1990. This nu technology has a goat potential to advance the capabilities of current computational mechanics. A first step to Implement this concept is to construct hierarchical models, a family of mathematical models which are sequentially connected by a key parameter of the problem under consideration and have different levels in modeling accuracy, and to investigate characteristics In their numerical simulation aspects. Among representative model problems to explore this concept are elastic structures such as beam-, arch-. plate- and shell-like structures because the mechanical behavior through the thickness can be approximated with sequential accuracy by varying the order of thickness polynomials in the displacement or stress fields. But, in the numerical analysis of hierarchical models, two kinds of errors prevail: the modeling error and the numerical approximation errors. To ensure numerical simulation quality, an accurate estimation of these two errors Is definitely essential. Here, a local a posteriori error estimator for elastic structures with thin domain such as plate- and shell-like structures Is derived using element residuals and flux balancing technique. This method guarantees upper bounds for the global error, and also provides accurate local error Indicators for two types of errors, in the energy norm. Comparing to the classical error estimators using flux averaging technique, this shows considerably reliable and accurate effectivity indices. To illustrate the theoretical results and to verify the validity of the proposed error estimator, representative numerical examples are provided.