• Computers and Concrete
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• v.30 no.4
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• pp.237-242
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• 2022

This paper is concerned with exponential stability in mean square for stochastic static neutral neural networks with varying delays. By using Lyapunov functional method and with the help of stochastic analysis technique, the sufficient conditions to guarantee the exponential stability in mean square for the neural networks are obtained and some results of related literature are extended.

• Bulletin of the Korean Mathematical Society
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• v.48 no.4
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• pp.725-736
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• 2011

By using the Lyapunov functional method, stochastic analysis, and LMI (linear matrix inequality) approach, the mean square exponential stability of an equilibrium solution of uncertain stochastic Hopfield neural networks with delayed is presented. The proposed result generalizes and improves previous work. An illustrative example is also given to demonstrate the effectiveness of the proposed result.

• Journal of applied mathematics & informatics
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• v.29 no.3_4
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• pp.781-802
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• 2011

Of concern in the paper is a Holling-Tanner predator-prey model with modified version of the Leslie-Gower functional response. Dynamical behaviours such as stability, permanence and Hopf bifurcation have been carried out deterministically. Using the normal form theory and center manifold theorem, the explicit formulae determining the stability and direction of Hopf bifurcation have been derived. The deterministic model is extended to a stochastic one by perturbing the growth equation of prey and predator by white and colored noises and finally the mean square stability of the stochastic model systems is investigated analytically. An extensive quantitative analysis has been performed based on numerical computation so as to validate the applicability of the proposed mathematical model.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1311-1325
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• 2011

In this paper the Milstein method is proposed to approximate the solution of a linear stochastic differential equation with Poisson-driven jumps. The strong Milstein method and the weak Milstein method are shown to capture the mean square stability of the system. Furthermore using some technique, our result shows that these two kinds of Milstein methods can well reproduce the stochastically asymptotical stability of the system for all sufficiently small time-steps. Some numerical experiments are given to demonstrate the conclusions.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.8
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• pp.584-593
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• 1987

A new combined approximation method using Routh stability array and mean-square error (MSE) method is proposed for deriving reduced-order z-transter functions for discrete time systems. The Routh stability array is used to obtain the reduced-order denominator polynomial, and the numerator polynomial is obtained by minimizing the mean-square error between the unit step responses of the original system and reduced model. The advantages of the new combined approximation method are that the reduced model is always stable provided the original model is stable and the initial and steady-state characteristics of the original model can be preserved in the reduced model.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.25 no.3E
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• pp.123-130
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• 2006

The adaptive echo canceller (AEC) has become an important component in speech communication systems, including mobile station. In these applications, the acoustic echo path has a long impulse response. We propose a running-average least mean square (RALMS) algorithm with a detection method for acoustic echo cancellation. Using colored input models, the result clearly shows that the RALMS detection algorithm has a convergence performance superior to the least mean square (LMS) detection algorithm alone. The computational complexity of the new RALMS algorithm is only slightly greater than that of the standard LMS detection algorithm but confers a major improvement in stability.

• Journal of the Korean Society of Propulsion Engineers
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• v.13 no.6
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• pp.34-40
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• 2009

The present study describes the methods and standards for the combustion stability assessment of a thrust chamber and a gas generator as parts of a liquid rocket engine. The first method uses a statistical approach through typical static combustion tests and the second one a dynamic assessment identifying decaying characteristics of pressure fluctuations excited by a pulse generating device. Based on accumulated test results, it is concluded that the maximal values for combustion stability are 3% of a chamber static pressure with a Root-Mean-Square value of pressure fluctuations, and 10 msec with a decay time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.133-142
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• 2014

The least mean square(LMS) algorithm has been popular owing to its simplicity, stability, and availability to implement. But it inherently has a problem of slow convergence speed, and the presence of a transfer function in the secondary path following the adaptive controller and the error path has been shown to generally degrade the stability and the performance of the LMS algorithm in applications of acoustical noise control. In general, in order to solve these problems, the filtered-x LMS (FX-LMS) type algorithms can be used and the bi-directional Filtered-x LMS(BFXLMS) algorithm is very attractive among them, which increase the convergence speed and the performance of the controller with nearly equivalent computation complexity. In this paper, a mathematical analysis for the BFXLMS algorithm is presented. In terms of view points of time domain, frequency domain, and stochastic domain, the characteristics and stabilities of algorithm is accurately analyzed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.97-104
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• 2007

Adaptive echo canceller(AEC) has become an important component in speech communication systems, including mobile phones and speech recognition. In these applications, the acoustic echo path has a long impulse response. We propose a moving-averge least mean square(MVLMS) algorithm with a detection method for acoustic echo cancellation. Using, the result of the tests that used colored input models clearly shows that the MVLMS detection algorithm has convergence performance superior to the least mean square(LMS) detection algorithm alone. Although the computational complexity of the new MVLMS algorithm is only slightly greater than that of the standard LMS detection algorithm, the new algorithm confers a significant improvement in stability.