• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.461-470
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• 2007

Parameter identification is studied in viscoelastic rods by solving an inverse problem numerically. The material properties of the rod, which appear in the constitutive relations, are recovered by optimizing an objective function constructed from reference strain data. The resulting inverse algorithm consists of an optimization algorithm coupled with a corresponding direct algorithm that computes the strain fields given a set of material properties. Numerical results are presented for two model inverse problems; (i)the effect of noise in the reference strain fields (ii) the effect of minimal reference data in space and/or time data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.233-242
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• 2023

The unscented Kalman filter (UKF), which is widely used to estimate the states of nonlinear dynamic systems, can be improved to realize robust system identification by using multiple sigma-point sets. When using Kalman filter methods for system identification, artificial noises must be appropriately selected to achieve optimal estimation performance. Additionally, an appropriate scaling factor for the sigma-points must be selected to capture the nonlinearity of the state-space model. This study entailed the use of Bouc-Wen hysteresis model to examine the nonlinear behavior of a single-degree-of-freedom oscillator. On the basis of the effects of the selected artificial noises and scaling factor, a new UKF method using multiple sigma-point sets was devised for improved robustness of the estimation over various signal-to-noise-ratio values. The results demonstrate that the proposed method can accurately track nonlinear system states even when the measurement noise levels are high, while being robust to the selection of artificial noise levels.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.150-154
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• 1993

A new method is proposed for obtaining Volterra kernals of a nonlinear system by use of a nonlinear systems by use of pseudorandom M-sequences and correlation technique. M-sequence is applied to a nonlinear technique. M-sequence is applied to a nonlinear system and the crosscorrelation function between the input and the output displays not only the linear impulse response of the linear part of the system, but also crosssections of the Volterra kernals of nonlinear system. Simulations are carried out for up to 3rd order Volterra kernal, and the results show a good agreement with the theoretical considerations.

• Smart Structures and Systems
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• v.24 no.6
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• pp.683-692
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• 2019

Traditional damage detection methods for nonlinear structures are often based on simplified models, such as the mass-spring-damper and shear-building models, which are insufficient for predicting the vibration responses of a real structure. Conventional global nonlinear finite element model updating methods are computationally intensive and time consuming. Thus, they cannot be applied to practical structures. A decentralized approach for identifying the nonlinear material parameters is proposed in this study. With this technique, a structure is divided into several small zones on the basis of its structural configuration. The unknown material parameters and measured vibration responses are then divided into several subsets accordingly. The structural parameters of each subset are then updated using the vibration responses of the subset with the Newton-successive-over-relaxation (SOR) method. A reinforced concrete and steel frame structure subjected to earthquake loading is used to verify the effectiveness and accuracy of the proposed method. The parameters in the material constitutive model, such as compressive strength, initial tangent stiffness and yielding stress, are identified accurately and efficiently compared with the global nonlinear model updating approach.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1711-1716
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• 2004

This paper presents identification and control of a 10-m antenna via accelerometers and angle encoder data. Artificial Neural Networks can be used effectively for the identification and control of nonlinear dynamical system such as a large flexible antenna. Some identification results are shown and compared with the results of conventional prediction error method. And we use a neural network inverse model for control the large flexible antenna. In the neural network inverse model, a neural network is trained, using supervised learning, to develop an inverse model of the antenna. The network input is the process output, and the network output is the corresponding process input. The control results show the validation of the ANN approach for identification and control of the 10-m flexible antenna.

• Journal of Electrical Engineering and Technology
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• v.5 no.4
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• pp.597-605
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• 2010

This paper presents a contribution to parameter identification of a non-linear system using a new strategy to improve the genetic algorithm (GA) method. Since cost function plays an important role in GA-based parameter identification, we propose to improve the simple version of GA, where weights of the cost function are not taken as constant values, but varying along the procedure of parameter identification. This modified version of GA is applied to the induction motor (IM) as an example of nonlinear system. The GA cost function is the weighted sum of stator current and rotor speed errors between the plant and the model of induction motor. Simulation results show that the identification method based on improved GA is feasible and gives high precision.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.7
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• pp.339-349
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• 2001

This paper suggests an optimal identification method for complex and nonlinear system modeling that is based on Fuzzy-Neural Networks(FNN). The proposed Hybrid Identification Algorithm is based on Yamakawa's FNN and uses the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. In this paper, the FNN modeling implements parameter identification using HCM algorithm and hybrid structure combined with two types of optimization theories for nonlinear systems. We use a HCM(Hard C-Means) clustering algorithm to find initial apexes of membership function. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are adjusted using hybrid algorithm. The proposed hybrid identification algorithm is carried out using both a genetic algorithm and the improved complex method. Also, an aggregated objective function(performance index) with weighting factor is introduced to achieve a sound balance between approximation and generalization abilities of the model. According to the selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity(distribution of I/O data), we show that it is available and effective to design an optimal FNN model structure with mutual balance and dependency between approximation and generalization abilities. To evaluate the performance of the proposed model, we use the time series data for gas furnace, the data of sewage treatment process and traffic route choice process.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.133-141
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• 1996

As the recording density of digital magnetic recording channels increased, the nonlinear distortion caused by inadequate rise-times, demagnetizing field, and overwrite effect during the recording process becomes significant, resulting in limitation on the recording rate. In this paper, a new method for identifying the nonlinear distortion in hihg-density magnetic recording channel using and i.i.d. (independent, identically distributed) inut is described. The proposed method does not require the operatin of large-size matrix inversion which are necessary for the conventional method, and it enables us to estimate nonlinear parameters transition shifts, which can be used to compensate the nonlinear distortion, is demonstrated by computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.26.6-26
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• 2001

In this paper we introduce a new algorithm that enables radial basis function network(RBFN) to be structured automatically and guarantees the stability of the RBFN. Because this new algorithm is efficient and also have the advantage of fast computational speed we adopt this algorithm as online learning scheme for uncertain nonlinear dynamical systems. Based on the fact that a 3-layered RBFN can represent a specific nonlinear function reasonably well by linearly combining a set of nonlinear and localized basis functions, we show that this RBFN can identify the nonlinear system very well without knowing the information of the system in advance.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.276-283
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• 2007

It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.