• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.135-145
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• 1995

A new algorithm was given to successively fit the multiexponential function/nonlinear function to data by a weighted least squares method, using Gauss-Newton, Marquardt, gradient and DUD methods for convergence. This study also considers the problem of linear-nonlimear weighted least squares estimation which is based upon the usual Taylor's formula process.

• Smart Structures and Systems
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• v.26 no.5
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• pp.631-640
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• 2020

In this paper, nonlinear vibration behaviors of multi-phase Magneto-Electro-Elastic (MEE) doubly-curved nanoshells have been studied employing Jacobi elliptic function method. The doubly-curved nanoshell has been modeled by using nonlocal elasticity and classic shell theory. An exact estimation of nonlinear vibrational behavior of smart doubly-curved nanoshell has been obtained via Jacobi elliptic function method. This method can incorporate the influences of higher order harmonics leading to an exact estimation of nonlinear vibration frequency. It will be indicated that nonlinear vibrational frequency of doubly-curved nanoshell relies on nonlocal effect, material composition, curvature radius, center deflection and electro-magnetic field.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1737-1743
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• 2013

This paper proposes the range and velocity of the object estimation method using a moving camera. Structure and motion (SaM) estimation is to estimate the Euclidean geometry of the object as well as the relative motion between the camera and object. Unlike the previous works, the proposed estimation method can relax the camera and object motion constraints. To this end, we arrange the dynamics of moving camera-moving object relative motion model in an appropriate form such that the nonlinear observer can be employed for the SaM estimation. Through both simulations and experiments we have confirmed the validity of the proposed estimation algorithm.

• Journal of the Korea Society for Simulation
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• v.2 no.1
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• pp.125-133
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• 1993

A statistical method is described for estimation of the unknown constants in a theory using both of the computer simulation data and the real experimental data, The best linear unbiased predictor based on a spatial linear model is fitted from the computer simulation data alone. Then nonlinear least squares estimation method is applied to the real experimental data using the fitted prediction model as if it were the true simulation model. An application to the computational nuclear fusion devices is presented, where the nonlinear least squares estimates of four transport coefficients of the theoretical nuclear fusion model are obtained.

• Steel and Composite Structures
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• v.20 no.4
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• pp.777-799
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• 2016

One of the major components for performance based seismic design is accurate estimation of critical seismic demand parameters. While nonlinear seismic analysis is the most appropriate analysis method for estimation of seismic demand parameters, this method is very time consuming and complex. Single mode pushover analysis method, N2 method and multi-mode pushover analysis method, modal pushover analysis (MPA) are two nonlinear static methods that have recently been used for seismic performance evaluation of few lateral load-resisting systems. This paper further investigates the applicability of N2 and MPA methods for estimating the seismic demands of ductile unstiffened steel plate shear walls (SPSWs). Three different unstiffened SPSWs (4-, 8-, and 15-storey) designed according to capacity design approach were analysed under artificial and real ground motions for Vancouver. A comparison of seismic response quantities such as, height-wise distribution of floor displacements, storey drifts estimated using N2 and MPA methods with more accurate nonlinear seismic analysis indicates that both N2 and MPA procedures can reasonably estimates the peak top displacements for low-rise SPSW buildings. In addition, MPA procedure provides better predictions of inter-storey drifts for taller SPSW. The MPA procedure has been extended to provide better estimate of base shear of SPSW.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.185-188
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• 1999

An estimation algorithm for vehicle driving load has been proposed in this paper. Driving load is an important factor in a vehicle's longitudinal motion control. An approach using an observer is introduced to estimate driving load based on inexpensive RPM sensors currently being used in production vehicles. Also, a torque estimation technique using nonlinear characteristic functions has been incorporated in this estimation algorithm. Using a nonlinear full vehicle simulation model, we study the effect of the driving load on longitudinal vehicle motion, and the performance of the estimation algorithm has been evaluated. The proposed estimation algorithm has good performance and robustness over uncertainties in the system parameters. An accurate estimate of the driving load can be very helpful in the development of advance vehicle control systems such as intelligent cruise control systems, CW/CA systems and smooth shift control systems.

• Journal of Ship and Ocean Technology
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• v.3 no.3
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• pp.25-44
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• 1999

Estimation efficiencies according to different sea trial are investigated in connection with sensitivity analysis, and new trial method is proposed which can improve the estimation efficiency of hydrodynamic derivatives. MMG Equation with Kijima's formula is used for simulation. Extended Kalman Filter is chosen for estimation technique and hydrodynamic derivatives of interest is limited to 12 of those in sway and yaw equations. Esso Osaka is selected for the test ship. Sensitivity analysis and estimation results based on conventional trials show that a more sensitive derivative gives more efficient estimation result. Sensitivities of nonlinear derivatives become pronounced in the trial where steady condition lasts longer such as turning test, while sensitivities of linear derivatives gas a larger values in the trial where unsteady condition lasts longer such as 10deg-10deg zigzag test. Consequently, in new method , named S-type trial, steady and unsteady condition are combined appropriately to increase sensitivities. Linear derivatives are estimated better in S-type trial and the estimation of nonlinear derivatives is improved to extent.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.187-190
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• 2003

In this paper, we address the robust adaptive backstepping controller using fuzzy neural network (FHIN) for a class of uncertain output feedback nonlinear systems with disturbance. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The state estimation is solved using K-fillers. All unknown nonlinear functions are approximated by FNN. The FNN weight adaptation rule is derived from Lyapunov stability analysis and guarantees that the adapted weight error and tracking error are bounded. The compensated controller is designed to compensate the FNN approximation error and external disturbance. Finally, simulation results show that the proposed controller can achieve favorable tracking performance and robustness with regard to unknown function and external disturbance.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• ETRI Journal
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• v.15 no.2
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• pp.35-51
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• 1993

This paper presents function approximation based on nonparametric estimation. As an estimation model of function approximation, a three layered network composed of input, hidden and output layers is considered. The input and output layers have linear activation units while the hidden layer has nonlinear activation units or kernel functions which have the characteristics of bounds and locality. Using this type of network, a many-to-one function is synthesized over the domain of the input space by a number of kernel functions. In this network, we have to estimate the necessary number of kernel functions as well as the parameters associated with kernel functions. For this purpose, a new method of parameter estimation in which linear learning rule is applied between hidden and output layers while nonlinear (piecewise-linear) learning rule is applied between input and hidden layers, is considered. The linear learning rule updates the output weights between hidden and output layers based on the Linear Minimization of Mean Square Error (LMMSE) sense in the space of kernel functions while the nonlinear learning rule updates the parameters of kernel functions based on the gradient of the actual output of network with respect to the parameters (especially, the shape) of kernel functions. This approach of parameter adaptation provides near optimal values of the parameters associated with kernel functions in the sense of minimizing mean square error. As a result, the suggested nonparametric estimation provides an efficient way of function approximation from the view point of the number of kernel functions as well as learning speed.