This paper proves that a certain class of nonconvex matrix inequalities is equivalent to linear matrix inequalities (LMIs) plus a nonconvex rank constraint. From the equivalence, this paper proposes two heuristic algorithms, that is extended linearization algorithms, to solve LMIs with a rank constraint using LMI-based approach. Reliability and efficiency of the algorithms are investigated stat...

Three methods in common use for finding confidence regions for the parameters of a nonlinear regression model are studied. These are the lack of fit method, the linearization method, and the maximum likelihood method. The objective is to determine which of the three methods is the preferred choice in applications. The conclusion is that the linearization method has the best structural character...

An algorithm for the computation of the stochastic non-stationary non-linear response of large FE-models is presented. In stochastic analysis, the response is described by the mean and covariance function and possibly by the probability distribution of the non-linear response. To estimate the covariance matrix, the method of Equivalent Statistical Linearization (EQL) is applied for linearizing ...

An analysis of the effects of time-sampling on the obseroer error lineariza-tion design methodology shows that requiring the method to be applicable for an open set of sampling times trioializes the class of allowable systems. Abstract-The effects of time-sampling on the solvability conditions for the observer linearization design methodology are investigated. It is shown that the class of syst...

In dealing with parametric nonlinear mixed effects models, intensive numerical integration often makes exact maximum likelihood estimation impractical given the current computing capacity. Algorithms based on linearization, such as the first order method and the conditional first order method, have the potential of producing highly inconsistent estimates, although numerically they are more effi...

The quadratic assignment problem (QAP) is one of the great challenges in combinatorial optimization. Linearization for QAP is to transform the quadratic objective function into a linear one. Numerous QAP linearizations have been proposed, most of which yield mixed integer linear programs (MILP). Kauffmann and Broeckx’s linearization (KBL) is the current smallest one in terms of the number of va...

This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback lin...