In practice, it is usually difficult to obtain the physical model of nonlinear, rotor-bearing systems due to uncertain nonlinearities. In order to solve this issue to conduct the analysis and design of nonlinear, rotor-bearing systems, in this study, a data driven NARX (Nonlinear Auto-Regressive with exogenous inputs) model is identified. Due to the lack of the random input signal which is requ...

The problem of identification of a nonlinear dynamic system by using multiple-input and multiple-output flexible neural tree (MIMO-FNT) is presented in this paper. This work is an extension of our previously multiple-input and singleoutput FNT model. FNT is a tree-structured neural networks which allows input variables selection, over-layer connections and different activation functions for dif...

In this thesis three algorithms for the estimation of parameters which occur nonlinearly in dynamic systems are presented. The first algorithm pertains to systems in discrete-time regression form. It is shown that the task of finding an update law for the parameter estimates can be solved numerically by the formulation of a quadratic programming problem. The algorithm does not depend on analyti...

This paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error’s Renyi entropy of order and minimization of a Csiszar distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi’s entropy is presented, and it is shown that the global minimum of this estimator is the same as the...

When performing nonlinear system identification few tools exist for the a priori nonlinear model structure selection of the nonlinear system. This paper presents a possible approach as a first step towards selecting a nonlineAtr system model structure, based on using the results of Lyapunov exponents, Poincar~ maps and dimension techniques. The approach is illusUated by applying it to the Chua ...

ph control is a challenging problem due to its highly nonlinear nature. in this paper the performances of two different adaptive global linearizing controllers (glc) are compared. least squares technique has been used for identifying the titration curve. the first controller is a standard glc based on material balances of each species. for implementation of this controller a nonlinear state est...

Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained...