• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.1
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• pp.34-42
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• 1992

This paper presents two fast least-squares lattice algorithms for adaptive nonlinear filters equipped with bilinear system models. The lattice filters perform a Gram-Schmidt orthogonalization of the input data and have very good numerical properties. Furthermore, the computational complexity of the algorithms is an order of magnitude snaller than previously algorithm is an order of magnitude smaller than previously available methods. The first of the two approaches is an equation error algorithm that uses the measured desired response signal directly to comprte the adaptive filter outputs. This method is conceptually very simple`however, it will result in biased system models in the presence of measurement noise. The second approach is an approximate least-squares output error solution. In this case, the past samples of the output of the adaptive system itself are used to produce the filter output at the current time. Results of several experiments that demonstrate and compare the properties of the adaptive bilinear filters are also presented in this paper. These results indicate that the output error algorithm is less sensitive to output measurement noise than the squation error method.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.31-41
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• 1993

In this paper, a fuzzy adaptive controller is proposed for the process with large delay time and unmodelled dynamics. The fuzzy adaptive controller consists of self tuning controller and fuzzy tuning part. The self tuning controller is designed with the continuous time GMV (generalized minimum variance) using emulator and weighted least square method. It is realized by the hybrid method. The controller has robust characteristics by adapting the inference rule in design parameters. The inference processing is tuned according to the operating point of the process having the nonlinear characteristics considering the practical application. We review the characteristics of the fuzzy adaptive controller through the simulation. The controller is applied to practical electric furnace. As a result, the fuzzy adaptive controller shows the better characteristics than the simple numeric self tuning controller and the PI controller.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.133-139
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• 2013

Convex combination of two adaptive filters is an efficient method to improve adaptive filter performances. In this paper, a subband convex combination method of two adaptive filters for fast convergence rate in the transient state and low steady state error is presented. The cost function of mixing parameter for a subband convex combination is defined, and from this, the coefficient update equation is derived. Steady state analysis is used to prove the stability of the subband convex combination. Some simulation examples in system identification scenario show the validity of the subband convex combination schemes.

• Proceedings of the KIEE Conference
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• 1994.11a
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• pp.322-325
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• 1994

Mareel and Bitmead proved the presence of chaotic signal in random noise by applying dead beat control theory to adaptive mechanism. In this paper robust adaptive theory is proposed. With the property of chaotic signal that has order and law, the proposed theory can enhance the control Performance by applying the recursive algorithm that uses dynamic relation which have small correlation. The performance of proposed algorithm is demonstrated with the computer simulation of position control of electric motor. In this simulation, the adaptive low is adopted to control electric motor and the Presence of chaotic signal in feedback signal is proved by using several method such as time series, fourier spectrum phase portrait method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.162-167
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• 2012

In power quality monitoring, it is very important to detect disturbances (sag, swell, transient, and interruption) accurately. In this paper, a detection method for power quality disturbances by combining the filter bank system and adaptive filter is proposed. To decompose power signal, binary tree structured filter bank system is designed. In the filter bank system, the fundamental filter bank(QMF bank) is used as a module in each decomposing level. An adaptive filter is used to improve the detection accuracy of disturbances for each subband signal. In the adaptive filter, the measure of estimated error change is used to detect singular points of power quality disturbances. Computer simulations were performed on synthetic signals which have disturbances to assess the performance of the proposed method.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.689-692
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• 2003

In this paper, we propose an adaptive beamforming algorithm for array antenna. The proposed beamforming algorithm, based on Block LMS (Block - Least Mean Squares) algorithm, has a variable step size from coefficient update. This method shows some advantages that the convergence speed is fast and the calculation time can reduced using a block LMS algorithm from frequency domain. As the adaptive parameter approaches a stationary state, it could reduce the number of filter coefficient update with the help of various step size. In this paper we compared the efficiency of the proposed algorithm with a standard LMS algorithm which is a representative method of adaptive beamforming.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.606-610
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• 2004

Although time-frequency analysis is useful for dispersive wave analysis, conventional methods such as the short-time Fourier transform do not take the dispersion phenomenon into consideration in the tiling of the time-frequency domain. The objective of this paper is to develop an adaptive time-frequency analysis method whose time-frequency tiling is determined with the consideration of signal dispersion characteristics. To achieve the adaptive time-frequency tiling, each of time-frequency atoms is rotated in the time-frequency plane depending on the local wave dispersion. To carry out this adaptive time-frequency transform, dispersion characteristics hidden in a signal are first estimated by an iterative scheme. To examine the effectiveness of the proposed method, the flexural wave signals measured in a plate were analyzed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.443-448
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• 2000

In this paper, we propose a novel adaptive feedforward controller for periodic disturbance and noise cancellation, with a frequency tracking capability. It can be added to an existing feedback control system without altering the original closed-loop characteristics, which is based on adaptive algorithm. We introduce novel algorithm "Constrained AFC(adaptive feedforward controller) algorithm" that increase the convergence region regardless of the delay in the closed loop system. In the algorithms, coefficients of the controller are adapted using the residuals of constrained structure which are defined in such a way that the coefficients become time invariant. The proposed algorithm not only estimate the magnitude and phase of the tonal disturbance and noise but also track the frequency of the tone, which changes in quasi-static manner. The frequency tracking algorithm uses the instantaneous frequency approach based on Hilbert transform. A number of computer simulations have been carried out in order to demonstrate the effectiveness of proposed method under various conditions.

• Korean Journal of Remote Sensing
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• v.8 no.1
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• pp.15-26
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• 1992

This paper describes an implementation of adaptive stereo matching for DBM generation. The matching method of two stereo satellite images to find corresponding points used in this paper is area-based matching, which is usually used in the field of making DBM. Same window size and search area used as in the conventional matching methods and we propose adaptive stereo matching algorithm in this paper. We cluster three areas which are consist of mountainous areas, cultivated areas and cities, and rivers and lakes by using proposed linear feature extracting method. These classified areas are matched by adaptive window size and search area, but rivers and lakes is excluded in this experiment. The matching time is three times faster than conventional methods.

• Journal of Information Processing Systems
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• v.15 no.3
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• pp.655-669
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• 2019

Supplier evaluation is of great significance in green supply chain management. Influenced by factors such as economic globalization, sustainable development, a holistic index framework is difficult to establish in green supply chain. Furthermore, the initial index values of candidate suppliers are often characterized by uncertainty and incompleteness and the index weight is variable. To solve these problems, an index framework is established after comprehensive consideration of the major factors. Then an adaptive weight D-S theory model is put forward, and a fuzzy-rough-sets-AHP method is proposed to solve the adaptive weight in the index framework. The case study and the comparison with TOPSIS show that the adaptive weight D-S theory model in this paper is feasible and effective.