In this work we consider the problem of blind system identification in noise driven by an independent and identically distributed (i.i.d) non-Gaussian signal generated from a deterministic nonlinear chaotic system. A new estimator for phase space volume (PSV) which is dynamic-based property chaos derived using maximum likelihood formulation. This novel PSV denoted as (ML-PSV). The Cramér Rao Lo...

This study focuses on the identification of aerodynamic coefficients through airdrop data based a known six-degree-of-freedom (6-DOF) model. Since precise modelling parafoil system is prerequisite for design guidance, navigation and control systems, accurate calculation vital. paper first presents rigid 6-DOF nonlinear model simplifies it by linearizing equations around equilibrium point, which...

In the field of control theory system identification is an important way of investigating and understanding the system. Several distinct approaches of system identification produce the different form of knowledge about the system. In system identification, neural network plays an important role. A novel technique has been considered to identify the nonlinear multi input multi output (MIMO) cros...

rapid urban growth is resulting into increase in travel demand and private vehicle ownership in urban areas. in the present scenario the existing infrastructure has failed to match the demand that leads to traffic congestion, vehicular pollution and accidents. with traffic congestion augmentation on the road, delay of commuters has increased and reliability of road network has decreased. four s...

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...

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...

The design goal of a control system is to influence the behavior of dynamic systems to achieve some pre-determinate objectives. A control system is usually designed on the premise that an accurate knowledge of a given object and environment cannot be obtained in advance. It usually requires suitable methods to address the problems related to uncertain and highly complicated dynamic system ident...