We present a novel identification framework that enables the use of first-order methods when estimating model parameters near a periodic orbit of a hybrid dynamical system. The proposed method reduces the space of initial conditions to a smooth manifold that contains the hybrid dynamics near the periodic orbit while maintaining the parametric dependence of the original hybrid model. First-order...

finding a suitable estimation of stability domain around stable equilibrium points is an important issue in the study of nonlinear dynamical systems. this paper intends to apply a set of analytical-numerical methods to estimate the region of attraction for autonomous nonlinear systems. in mechanical and structural engineering, autonomous systems could be found in large deformation problems or c...

In this paper, Kolmogorov-Sinai entropy is studied using mathematical modeling of an observer $ Theta $. The relative entropy of a sub-$ sigma_Theta $-algebra having finite atoms is defined and then   the ergodic properties of relative  semi-dynamical systems are investigated.  Also,  a relative version of Kolmogorov-Sinai theorem  is given. Finally, it is proved  that the relative entropy of a...

in this paper, two sets of sufficient conditions are obtained to ensure the existence and stability of a unique equilibrium point of unforced first-order fuzzy relational dynamical systems by using two different approaches which are both based on the fuzzy relational matrix of the model.in the first approach, the equilibrium point of the system is one of the centers of the related membership fu...

In this study, the identification and control of a Bioreactor Plant using neural networks is considered with three different control strategies, namely, Inverse Control Strategy, Self-Learning Control and Dynamical Neural Units for control of nonlinear dynamical systems. The performance of these methods are compared using several comparison measures.

Identification and Control of Non-linear dynamical systems is a challenging probelm to the control engineers. The function approximation capability of artificial neural networks can be very effective in designing efficient system idnetification models and controllers for non-linear systems. Narendra and Parthsarathy [1] has suggested models for both identification and control of non-linear syst...

This study is focused on the automatic modelling of linear dynamical systems based on system output observations. Identification of a differential equation degree and its parameters requires the initial point because the output of the dynamical system for the given input depends on the initial point value. Generally, it is impossible to estimate the system state and its derivatives using the ob...

The identification of the limiting factors in the dynamical behavior of complex systems is an important interdisciplinary problem which often can be traced to the spectral properties of an underlying network. By deriving a general relation between the eigenvalues of weighted and unweighted networks, here I show that for a wide class of networks the dynamical behavior is tightly bounded by few n...

in this paper we study shadowing property for sequences of mappings on compact metric spaces, i.e. nonautonomous discrete dynamical systems. we investigate the relation of  weak contractions with shadowing and h-shadowing property.