• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.5
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• pp.3434-3439
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• 2015

As a criterion of information theoretic learning, the Euclidean distance (ED) of two error probability distribution functions (minimum ED of error, MEDE) has been adopted in nonlinear (decision feedback, DF) supervised equalizer algorithms and has shown significantly improved performance in severe channel distortion and impulsive noise environments. However, the MEDE-DF algorithm has the problem of heavy computational complexity. In this paper, the recursive ED for MEDE-DF algorithm is derived first, and then the feed-forward and feedback section gradients for weight update are estimated recursively. To prove the effectiveness of the recursive gradient estimation for the MEDE-DF algorithm, the number of multiplications are compared and MSE performance in impulsive noise and underwater communication environments is compared through computer simulation. The ratio of the number of multiplications between the proposed DF and the conventional MEDE-DF algorithm is revealed to be $2(9N+4):2(3N^2+3N)$ for the sample size N with the same MSE learning performance in the impulsive noise and underwater channel environment.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.74_76
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• 2009

This paper presents a discrete wavelet analysis based algorithm to address the fault impedance calculation under transient state in radial power distribution networks. The fault impedances have been derived under different fault conditions. Furthermore, a recursive fault distance estimation method is proposed utilizing the measured fault impedance and power line parameters. The proposed scheme can resolve the errors caused by the non-homogeneous power lines, the presence of lateral loads since, the fault impedance will always be updated with the recursive form. For the verification of the proposed scheme, a filed test has been peformed with varying fault resistances in the 22.9(kV) radial system. Power meters and fault locators were installed at the substation. It was figured out that the performance of the discrete wavelet and the recursive scheme are very good even for high fault resistance condition.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.279-281
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• 2006

A Voltage Instability Predictor(VIP) estimates the proximity of a power system to voltage collapse in real time. Voltage Instability Index(Z-index) from VIP algorithm is estimated using LS(Least Square) method. But this method has oscillations and noise of result due to the system's changing conditions. To suppress oscillations, a larger data window needs to be used. In this paper. I propose the new other method which improves that weakness. It uses RLS(Recursive Least Square) to estimate voltage instability index without a large moving data window so this method is suitable for on-line monitor and control in real time. In order to verify effectiveness of the algorithm using RLS method, the method is tested on HydroQuebec system in real time digital simulator(HYPERSIM).

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.23-28
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• 1998

We propose a nonlinear feedback controller that can control the induction motors with high dynamic performance by means of decoupling of motor speed and rotor flux. The nonlinear feedback controller needs the information on some motor parameters. Among them, rotor resistance varies greatly with machine temperature. A new recursive adaptation algorithm for rotor resistance which can be applied to our nonlinear feedback controller is also presented in this paper. The recursive adaptation algorithm makes the estimated value of rotor resistance track its real value. Some simulation results show that the adaptation algorithm for rotor resistance is robust against the variation of stator resistance and mutual inductance. In addition, it is computationally simple and has small estimation errors. To demonstrate the practical significance of our results, we present some experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.650-655
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• 2016

In this paper, a novel RLS-based DOB (Recursive Least Squares Disturbance Observer) scheme is proposed to improve the performance of DOB for nominal model identification. A nominal model can be generally assumed to be a second order system in the form of a proper transfer function of an ARMA (Autoregressive Moving Average) model. The RLS algorithm for the model identification is proposed in association with DOB. Experimental studies of the balancing control of a one-wheel robot are conducted to demonstrate the feasibility of the proposed method. The performances between the conventional DOB scheme and the proposed scheme are compared.

• Proceedings of the KIPE Conference
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• 2013.11a
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• pp.200-201
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• 2013

This paper investigates a new approach for Li-Ion battery state-of-charge (SOC) estimation using sliding mode observer (SMO) technique including parameters aging compensation via recursive least squares (RLS). The main advantages of this approach would be low computational load, easiness of implementation along with the robustness of the method for internal battery model parameters estimation. The proposed algorithm was first tested on a set of acquired battery data using implementation in Simulink and later developed as C-code module for firmware application.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.10
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• pp.438-444
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• 1986

The signal can be modeled as a linear combination of its past values and present and past values of a hypothetical input to system whose output is given signal. Using this model spectral estimation problem can be reduced to estimate the ARMA parameters. This paper presents recursive modified instrumental variable algorithm which can estimate AR and MA parameters. For more accurate estimation, overdetermined modified IV algorithm is also derived. Computer simulations are presented to illustrate the above methods.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.785-787
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• 1999

In this paper, recursive least square algorithm is presented to estimate the parameters of step motor under low-speed operation. Parameter estimation is important for compensating the input current by calculating the ratio of the motor torque constant and detent torque constant that causes torque-ripple in low-speed applications. On-line parameter estimation process is a preliminary procedure to apply step motor to adaptive control. Computer simulation shows that the estimated parameters converge in finite time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.833-842
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• 2017

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2172-2177
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• 2015

The DF-MZEP-CME (decision feedback - maximum zero-error probability for constant modulus errors) algorithm that makes the probability for constant modulus error (CME) close to zero and employs decision feedback (DF) structures shows more improved performance in channel distortion compensation. However the DF-MZEP-CME algorithm has a computational complexity proportional to a sample size for probability estimation and this property plays a role of an obstacle in practical implementation. In this paper, the gradient of DF-MZEP-CME is proposed to be estimated recursively and shown to solve the computational problem by making the algorithm independent of the sample size. For a sample size N, the conventional method has 10N multiplications but the proposed has only 20 regardless of N. Also the recursive gradient estimation for weight update is kept in continuity from the initial state to the steady state without any error propagation.