Nonlinear system online identification via dynamic neural networks is studied in this paper. The main contribution of the paper is that the passivity approach is applied to access several new stable properties of neuro identification. The conditions for passivity, stability, asymptotic stability, and input-to-state stability are established in certain senses. We conclude that the gradient desce...

International Journal of Control Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713393989 Neural networks for nonlinear dynamic system modelling and identification S. Chen a; S. A. Billings b a Department of Electrical Engineering, University of Edinburgh, Edinburgh, EH9 3JL, U.K. b Department of Automatic Co...

The Bussgang coefficient is calculated for a memoryless nonlinear system and the concept extended to a dynamic system modeled by a Volterra Series and for Gaussian Inputs. The theory obtained is then applied to a simple system and the underlying linear system obtained for different system configurations.

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than nec...

System-identification methods compose a mathematical model, or series of models, from measurements of inputs and outputs of dynamic systems. The estimated models allow the characterization of the response of the overall aircraft or component subsystem behavior. This paper discusses the use of System Identification Toolbox of MATLAB/SIMULINK for the estimation of aircraft flight dynamics in long...

A system for market identification (SMI) is presented. The resulting representations are multivariable dynamic demand models. The market specifics are analyzed. Appropriate models and identification techniques are chosen. Multivariate static and dynamic models are used to represent the market behavior. The steps of the first stage of SMI, named data preprocessing, are mentioned. Next, the secon...

In this paper we consider the problem of whether a nonlinear system has dynamic noise and then estimate the level of dynamic noise to add to any model we build. The method we propose relies on a nonlinear model and an improved least squares method recently proposed on the assumption that observational noise is not large. We do not need any a priori knowledge for systems to be considered and we ...

The paper presents the application results concerning the fault detection of a dynamic process using linear system identification and model–based residual generation techniques. The first step of the considered approach consists of identifying different families of linear models for the monitored system in order to describe the dynamic behaviour of the considered process. The second step of the...

In this contribution different methods to excite dynamic systems are presented. Model-based/model-free and offline/online design plans are discussed and methods to optimize design plans with respect to the intended use are introduced. The presented design plans are appropriate to identify the dynamics of combustion engines and are evaluated by simulation and practical examples.

This paper explores training and initialization aspects of dynamic neural networks when applied to the nonlinear system identification problem. A well known dynamic neural network structure contains both output states and hidden states. Output states are related to the outputs of the system represented by the network. Hidden states are particularly important in allowing dynamic neural networks ...