• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

• Structural Engineering and Mechanics
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• v.6 no.5
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• pp.473-484
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• 1998

The first and second moments of response variables for SDOF systems with hysteretic nonlinearity are obtained by a direct linearization procedure. This adaptation in the implementation of well-known statistical linearization methods, provides concise, model-independent linearization coefficients that are well-suited for numerical solution. The method may be applied to systems which incorporate any hysteresis model governed by a differential constitutive equation, and may be used for zero or non-zero mean random vibration. The implementation eliminates the effort of analytically deriving specific linearization coefficients for new hysteresis models. In doing so, the procedure of stochastic analysis is made independent from the task of physical modeling of hysteretic systems. In this study, systems with three different hysteresis models are analyzed under various zero and non-zero mean Gaussian White noise inputs. Results are shown to be in agreement with previous linearization studies and Monte Carlo Simulation.

• Transactions of the Korean Society of Mechanical Engineers
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• v.10 no.5
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• pp.742-750
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• 1986

The lateral vibrating motion of a railway vehicle over a certain critical speed is a well known problem in the field of train dynamics. It is known that the train equations of motion are strongly coupled and highly nonlinear with the motion and causing that it is very difficult to solve the equations simultaneously. In this paper, a 8 degree of feedom model of a railway vehicle was suggested to solve the rail vehicle lateral motion. In stead of solving the nonlinear equation simultaneously, statistical linearization technique was adopted to solve those equations. The analysis results from the statistical linearization method were directly compared with those from direct nonlinear equations and found that the linearization technique can be very effective and economical for railroad vehicle analysis. By the way, it was found that the analysis results can analytically explain the intermittent hunting phenomena which has been frequently observed in experiments.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.1
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• pp.37-41
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• 1995

In order to analyze the rolling motion of a ship in random beam waves we use the partial stochastic linearization method. The quadratic damping and the nonlinear restoring moments given by the odd polynomials up to the 11th order are added to a single degree of freedom linear equation of roll motion. The irregular excitation moment is assumed to be the Gaussian white noise. The statistical characteristics of the response by the partial stochastic linearization method is compared with results by the equivalent linearization method and Monte Carlo simulation. It is fecund that the partial stochastic linearization method is not necessarily superior to the equivalent linearization method.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.39-45
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• 1993

In order to analyze the rolling motion of a ship in random beam waves we have used the equivalent linearization method. The quadratic nonlinear damping, the cubic and quintic nonlinear restoring moments were added to a single degree of freedom linear equation of roll motion. The irregular excitation moment was assumed to be the Gaussian white noise. The statistical characteristic of the response by the equivalent linearization method was compared with the simulation result.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.9
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• pp.929-936
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• 1991

An estimator for a discrete nonlinear system is derived in the sense of minimum mean square error. An optimal estimator for nonlinear system is very difficult to find and it will be infinite dimensional even if it is found. It has been known that the statistical linearization technique makes it possible to obtain a finite dimensional estimator. In this paper, the procedure of its derivation using the statistical linearization technique that gives an exact mean and variance information is introduced in the sense of minimum mean square error. The derived estimator cannot be clainmed to be globally optimal estimator because it uses the Gaussian assumption to the non-Gaussian distributed nonlinear output. However, the proposed filter exhibits a better performance compared to extended Kalman filter. Simulation results of a simple example present the improvement of the proposed filter in convergent property over the extended Kalman filter.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.163-168
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• 2009

Given the specific mean shift outlier model, the standard approaches to obtaining test statistics for outliers are discussed. Accuracy of outlier tests is investigated using subset curvatures. These subset curvatures appear to be reliable indicators of the adequacy of the linearization based test. Also, we consider obtaining graphical summaries of uncertainty in estimating parameters through confidence curves. The results are applied to the problem of assessing the accuracy of outlier tests.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.274-279
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• 1991

The development of a guided weapon system, such as a tactical missile, requires a performance analysis of a nonlinear system. Generally, the Monte Carlo analysis method is used for this purpose. The limitation of this method, a large number of simulations, for a nonlinear system performance analysis strongly motivated the development of a more efficient analytic technique. In this paper, the statisfical linearization methods is used for the performance analysis to the guided weapon system with the help of covariance analysis technique. Because the statistical linearization methods cannot be used to the look-up table nonlinear form such as aerodynamic coefficients, the second order polynomial representations is obtained from the table using the Lagrange interpolating polynomial and linearized statistically. Simple simulations about initial state conditions and random component in guidance command shows the results of this technique.

• Earthquakes and Structures
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• v.2 no.4
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• pp.377-396
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• 2011

In seismic design and structural assessment using the displacement-based approach, real structures are simplified into equivalent single-degree-of-freedom systems with equivalent properties, namely period and damping. In this work, equations for the optimal pair of equivalent properties are derived using statistical procedures on equivalent linearization and defined in terms of the ductility ratio and initial period of vibration. The modified Clough hysteretic model and 30 artificial accelerograms, compatible with the acceleration spectra for firm and soft soils, defined by the Japanese Design Specifications for Highway Bridges are used in the analysis. The results obtained with the proposed equations are verified and their limitations are discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.17-27
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• 2014

As the importance of Cooperative Engagement Capability and network-centric warfare has been dramatically increasing, it is necessary to develop distributed tracking systems. Under the development of distributed tracking systems, it requires tracking filters and data fusion theory for nonlinear systems. Therefore, in this paper, the problem of nonlinear track fusion, which is suitable for distributed networks, is formulated, four algorithms to solve the problem of nonlinear track fusion are introduced, and performance of introduced algorithms are analyzed. It is a main problem of nonlinear track fusion that cross-covarinaces among multiple platforms are unknown. Thus, in order to solve the problem, two techniques are introduced; a simplification technique and a approximation technique. The simplification technique that help to ignore cross-covariances includes two algorithms, i.e. the sample mean algorithm and the Millman formula algorithm, and the approximation technique to obtain approximated cross-covariances utilizes two approaches, by using analytical linearization and statistical linearization based on the sigma point approach. In simulations, BCS fusion is the most efficient scheme because it reduces RMSE by approximating cross-covariances with low complexity.