The task of identifying the non-linearity of ferroelectric devices by using a ‘Quartic Double Well’ (QDW) model is addressed. A strategy for the estimation of model parameters fitting the dynamic behaviour of the device in a large range of frequencies is discussed. An experimental set-up for investigating the dynamic behaviour of a ferroelectric device is described and results are presented sho...

We derive a cost functional for estimating the inverse of the observation function in nonlinear dynamical systems. Limiting our search to invertible observation functions confers numerous benefits, including a compact representation and no local minima. Our approximation algorithms for optimizing this cost functional are fast, and give diagnostic bounds on the quality of their solution. Our met...

This note derives some sufficient conditions to ensure that the whole switched nonlinear system is input-to-state stabilizable (ISS) when each mode is ISS. Both cases that the switchings of system modes coincide exactly and do not coincide with those of the corresponding controllers are considered. For the latter, a model-based identification scheme is used to identify the system modes. The pro...

â  abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...

By reversing paradigms that normally utilize mathematical models as the basis for nonlinear adaptive controllers, this article describes using the controller to serve as a novel computational approach for mathematical system identification. System identification usually begins with the dynamics, and then seeks to parameterize the mathematical model in an optimization relationship that produces ...

System identification is the process of deducing a mathematical model of the internal dynamics of a system from observations of its outputs. The computer program PRET automates this process by building a layer of artificial intelligence (AI) techniques around a set of traditional formal engineering methods. PRET takes a generate-and-test approach, using a small, powerful meta-domain theory that...

Modeling and identification of mechanical systems constitute an essential stage in practical control design and applications. Controllers commanding systems that operate at varying conditions or require high precision operation raise the need for a nonlinear approach in modeling and identification. Most mechanical systems used in industry are composed of masses moving under the action of positi...

System identification is the theory and methods of establishing mathematical models of the systems. As one of the key issues of system and control science, system identification has been widely applied to the design and analysis of the control system. Accordingly, system identification becomes one of the current active subjects. Currently, parameter estimation of the nonlinear system models is ...

Various methods can be used for nonlinear, dynamic-system identification and Gaussian process (GP) model is a relatively recent one. The GP model is an example of a probabilistic, nonparametric model with uncertainty predictions. It possesses several interesting features like model predictions contain the measure of confidence. Further, the model has a small number of training parameters, a fac...