The development of statistically efficient and computationally practicable estimation methods for the analysis of structural equation models with multiple nonlinear effects has been called for by substantive researchers in psychology, marketing research, and sociology. But the development of efficient methods is complicated by the fact that a nonlinear model structure implies specifically nonno...

Parameter estimation method of Jelinski-Moranda (JM) model based on weighted nonlinear least squares (WNLS) is proposed. The formulae of resolving the parameter WNLS estimation (WNLSE) are derived, and the empirical weight function and heteroscedasticity problem are discussed. The effects of optimization parameter estimation selection based on maximum likelihood estimation (MLE) method, least s...

Linear motor is one of the most important drive units for high-speed machining centers. Nonlinear friction is an important factor affecting the positioning accuracy of linear motor drive system. One of the most important topics for controlling linear motor is how to accurately estimate the parameters of the friction and effectively compensate it. In this paper, to solve the problem existing in ...

Simultaneous Nonlinear Model Predictive Control and State Estimation: Theory and Applications by David A. Copp As computational power increases, online optimization is becoming a ubiquitous approach for solving control and estimation problems in both academia and industry. This widespread popularity of online optimization techniques is largely due to their abilities to solve complex problems in...

This paper treats the problem of estimating simultaneously the state and the unknown inputs of a class of nonlinear discrete-time systems. An observer design method for nonlinear Lipschitz discrete-time systems is proposed. By assuming that the linear part of this class of systems is time-varying, the state estimation problem of nonlinear system is transformed into a state estimation problem fo...

An application of wavelet neural network/wavenet in control problems of nonlinear systems is investigated in this study. A wavelet neural network is constructed as an alternative architecture to a neural network to approximate a nonlinear system. Based on approximation capability of wavelet network, a suitable adaptive control law and parameter updating algorithm as applied to nonlinear system ...

Minimax parameter estimation aims at characterizing the set of all values of the parameter vector that minimize the largest absolute deviation between the experimental data and the corresponding model outputs. It is well known, however, to be extremely sensitive to outliers in the data resulting, e.g., of sensor failures. In this paper, a new method is proposed to robustify minimax estimation b...

The traditional view adopted by statistics and econometrics texts is that, in order to solve a nonlinear least squares (NLS) or nonlinear maximum likelihood (NML) problem, a researcher need only use a computer program. This point of view maintains two implicit assumptions: (1) one program is as good as another; and (2) the output from a computer is reliable. Both of these maintained assumptions...

The problem of nonlinear estimation is reexamined, and a new semi-parametric representation of uncertainty called the Biscay distribution is presented. The Biscay distribution is combined with the extended Kalman filter (EKF) and a new filtering paradigm called the Biscay distribution filter (BDF) is developed. The BDF is provably optimal for linear estimation and generalizes naturally to nonli...