• The Transactions of the Korean Institute of Electrical Engineers P
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• v.52 no.2
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• pp.81-84
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• 2003

This paper presents a proper rational transfer function synthesis in the continuous time system from noisy measurements. The proposed method identifies the coefficients vector of the transfer function from an overdetermined linear system that develops from rearranging the two dimensional system matrices and output vectors obtained from the observed frequency responses. By computer simulation, the performance improvement is verified.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1872-1879
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• 2016

The techniques to recycle and reuse plastics attract public attention. These public attraction and needs result in improving the recycling technique. However, the identification technique for black plastic wastes still have big problem that the spectrum extracted from near infrared radiation spectroscopy is not clear and is contaminated by noise. To overcome this problem, we apply Raman spectroscopy to extract a clear spectrum of plastic material. In addition, to improve the classification ability of fuzzy Radial Basis Function Neural Networks, we apply supervised learning based clustering method instead of unsupervised clustering method. The conditional fuzzy C-Means clustering method, which is a kind of supervised learning based clustering algorithms, is used to determine the location of radial basis functions. The conditional fuzzy C-Means clustering analyzes the data distribution over input space under the supervision of auxiliary information. The auxiliary information is defined by using k Nearest Neighbor approach.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.33.1-33
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• 2001

We address a novel approach to identify a nonlinear dynamic system for Wiener models, which are composed of a linear dynamic system part followed by a nonlinear static part. The aim of system identification here is to provide the optimal mathematical model of both the linear dynamic and the nonlinear static parts in some appropriate sense. Assuming the nonlinear static part is invertible, we approximate the inverse function by a piecewise linear function. We estimate the piecewise linear inverse function by using the evolutionary computation approach such as genetic algorithm (GA) and evolution strategies (ES), while we estimate the linear dynamic system part by the least squares method. The results of numerical simulation studies indicate the usefulness of proposed approach to the Wiener model identification.

• East Asian mathematical journal
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• v.16 no.2
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• pp.317-330
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• 2000

In this paper we consider the optimal control problem of both operators and parameters for nonlinear hyperbolic systems. For the identification problem, we show that for every value of the parameter and operators, the optimal control problem has a solution. Moreover we obtain the necessary conditions of optimality for the optimal control problem on the system.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1862-1863
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• 2006

In this paper, A new tuning method for IMC-PID controller is proposed with the identification using the relay method from closed-loop transfer function. It is considered a second-order plus delay time(SOPDT) model and selected a third-order plus delay time transfer function model as a target function. The filter function is derived from the suitable target function to satisfy the design specifications. A robustness test was done to verify the robust-stability.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.7
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• pp.1156-1165
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• 1997

Complex modal testing is employed for the in-situ parameter identification of a four-axis active magnetic bearing system while the system is in operation. In the test, magnetic bearings are used as exciters as well as actuators for feedback control. The experimental results show that the directional frequency response function, which is properly defined in the complex domain, is a powerful tool for identification of bearing as well as modal parameters. It is also shown that the position and current stiffnesses can be accurately estimated using the relations between the measured forces, displacements, and currents.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.127-139
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• 1996

In this paper we propose a new controller design method using hybrid fuzzy-neural netowrk and neural network identification in order ot control systems which are more and more getting nonlinearity. Proposed method performs, for a nonlinear plant with unknown functions, hybird identification using a fuzzy-neural network and a neural network, and then a stable nonlinear controller is designed with those identified informations. To identify a nonlinear function, which is directly related to input signals, we can use a neural network which is satisfied with the proposed stable condition. To identify a nonlinear function, which is not directly related to input signals, we can use a fuzzy-neural network which has excellent identification characteristics. In order to verify excellent control performances of the proposed method, we compare the porposed control method with a conventional neural network control method through simulations and experiments with one link manipulator.

• MALSORI
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• no.64
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• pp.121-135
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• 2007

Speech signal is distorted by channel characteristics or additive noise and then the performances of speaker or speech recognition are severely degraded. To cope with the noise problem, we propose a modified HMM decoder algorithm using SNR-based observation confidence, which was successfully applied for GMM in speaker identification task. The modification is done by weighting observation probabilities with reliability values obtained from SNR. Also, we apply PSO (particle swarm optimization) method to the confidence function for maximizing the speaker identification performance. To evaluate our proposed method, we used the ETRI database for speaker recognition. The experimental results showed that the performance was definitely enhanced with the modified HMM decoder algorithm.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.712-717
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• 2005

This paper presents a system identification method for multi-input, multi-output (MIMO) systems, by which a rational polynomial transfer function model is identified from experimentally determined frequency response function data. Analytically determined information is incorporated in this method to obtain a more reliable model, even in the frequency range where the excitation energy is limited. To verify the suggested method, shaking table test for an actively controlled two-story, bench-scale building employing an active mass damper is conducted. The results show that the proposed method is quite effective and robust for system identification of MIMO systems.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.131-140
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• 1994

In order to find the identification function from data and to apply the identification function for the pattern classification, we consider the existing problem of the number of patterns in such data. In this paper, a new criteria for the identification of Gaussaian mixture distribution could be established as a charateristic of the sample variance, which is a bootstrap estimate of the sample variance. We examine the properties and fittness of the criteria through a large scale of computer simulations.