• Journal of the Korean Statistical Society
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• v.13 no.2
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• pp.107-113
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• 1984

In a linear regression model the idependent variables are frequently subject to measurement errors. For this case, the problem of estimating unknown parameters has been extensively discussed in the literature while very few has been concerned with the effect of measurement errors on prediction. This paper investigates the behavior of the predicted values of the dependent variable in terms of the average mean square error of prediction (AMSEP). AMSEP may be used as a criterion for selecting an appropriate estimation method, for designing an estimation experiment, and for developing cost-effective future sampling schemes.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2E
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• pp.64-68
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• 2008

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square(VEDNSS LMS) algorithm. Adopting VEDNSS LMS results in higher system complexity, but noise is reduced providing fast convergence speed Mathematical analysis demonstrates that tap coefficient misadjustment converges. This is confirmed by computer simulation with the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.333-336
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• 2001

This paper reports that the Lambert W function applies to a non-iterative design of minimum mean square-error scalar quantizers for a Laplacian source. The contribution of the paper is in the reduction of the time needed for the design and the increased accuracy in resulting quantization points and thresholds, because the algorithm is non-iterative and the Lambert W function can be evaluated as accurately as desired.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1714-1725
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• 1993

This paper analyzes the properties of such algorithm that corresponds to the nonlinear adaptive algorithm with additional update terms, parameterized by the scalar factors a1, a2, a3 and Presents its structure. The analysis of convergence leads to eigenvalues of the transition matrix for the mean weight vector. Regions in which the algorithm becomes stable are demonstrated. The time constant is derived and the computational complexities of MLMS algorithms are compared with those of the conventional LMS, sign, LFG, and QFG algorithms. The properties of convergence in the mean square are analyzed and the expressions of the mean square recursion and the excess mean square error are derived. The necessary condition for the CFG algorithm to be stable is attained. In the computer simulation applied to the system identification the CFG algorithm has the more computation complexities but the faster convergence speed than LMS, LFG and QFG algorithms.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.25-32
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• 2017

For repairable software systems, the Mean Time Between Failure (MTBF) is used as a measure of software system stability. Therefore, the evaluation of software reliability requirements or reliability characteristics can be applied MTBF. In this paper, we want to compare MTBF in terms of parameter estimation using Makeham life distribution. The parameter estimates used the least square method which is regression analyzer method and the maximum likelihood method. As a result, the MTBF using the least square method shows a non-decreased pattern and case of the maximum likelihood method shows a non-increased form as the failure time increases. In comparison with the observed MTBF, MTBF using the maximum likelihood estimation is smallerd about difference of interval than the least square estimation which is regression analyzer method. Thus, In terms of MTBF, the maximum likelihood estimation has efficient than the regression analyzer method. In terms of coefficient of determination, the mean square error and mean error of prediction, the maximum likelihood method can be judged as an efficient method.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.159-162
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• 2010

This paper presents a new Kurtosis driven Variable Step-Size Normalized Least Mean Square (KVSSN-LMS) algorithm to prevent repeater from oscillation due to feedback signal of radio frequency (RF) repeater. To get better Mean Square Error (MSE) performance, step-size is adjusted using the kurtosis. The proposed algorithm shows the better performance of steady state MSE. The proposed algorithm shows a better ERLE performance than that of KVSS-LMS, VSS-NLMS, NLMS algorithms.

• The Journal of the Acoustical Society of Korea
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• v.16 no.1E
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• pp.41-48
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• 1997

The very purpose of subband codec is the attainment of data rate compression through the use of quantizer and optimum bit allocation for each decimated signal. Yet the question of floating-point quantization effects in subband codec has received scant attention. There has been no direct focus on the analysis of quantization errors, nor on design with quantization errors embedded explicitly in the criterion. This paper provides a rigorous theory for the modelling, analysis and optimum design of the general M-band subband codec in the presence of the floating-point quantization noise. The floating-point quantizers are embedded into the codec structure by its equivalent multiplicative noise model. We then decompose the analysis and synthesis subband filter banks of the codec into the polyphase form and construct an equivalent time-invariant structure to compute exact expression for the mean square quantization error in the reconstructed an equivalent time-invariant structure to compute exact expression for the mean square quantization error in the reconstructed output. The optimum design criteria of the subband codec is given to the design of the analysis/synthesis filter bank and the floating-point quantizer to minimize the output mean square error. Specific optimum design examples are developed with two types of filter of filter banks-orthonormal and biorthogonal filter bank, along with their perpormance analysis.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.53-58
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• 2012

In this paper, we evaluate and analyze representative estimation methods for the sparse channel. In order to evaluate error performance of matching pursuit(MP) and minimum mean square error(MMSE) algorithm, lower bound of MMSE is determined by Cramer-Rao bound and compared with upper bound of MP. Based on analysis of those bounds, mean square error of MP which is effective in the estimation of sparse channel can be larger than that of MMSE according to the number of estimated tap and signal-to-noise ratio. Simulation results show that the performances of both algorithm are reversed on the sparse channel with Rayleigh fading according to signal-to-noise ratio.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.95.6-95
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• 2002

1. Introduction 2. Basic Concepts 2.1 Signal Model 2.2. Least-Mean-Square Adaptation Algorithm 3. Minimum Mean-Square Error 4. Bayesian Beamformer References