This study presents a fusion of data-driven and physics-driven methodologies of energetic electron flux forecasting in the outer radiation belt. Data-driven NARMAX (Nonlinear AutoRegressive Moving Averages with eXogenous inputs) model predictions for geosynchronous orbit fluxes have been used as an outer boundary condition to drive the physics-based Versatile Electron Radiation Belt (VERB) code...

The investigation of robot-environment interaction is the main aim of the RobotMODIC project at the Universities of Essex and Sheffield. The methods developed under this project model and characterise all aspects relevant to the robot’s operation: modelling of sensor perception (“environment identification” or simulation), sensor modelling, and task modelling. In this paper we describe a new pr...

Model reduction methods aim to describe complex dynamic phenomena using only relevant dynamical variables, decreasing computational cost, and potentially highlighting key mechanisms. In the absence of special features such as scale separation or symmetries, time evolution these variables typically exhibits memory effects. Recent work has found a variety data-driven model be effective for repres...

Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real...

This paper deals with multiobjective nonlinear system identification applied when modelling the relation of firing angle and equivalent reactance of a thyristor controlled series capacitor (TCSC). The mathematical representation chosen is NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) due to its capability in modelling nonlinear systems and in using prior information. Th...

In this paper, we generalize retrospective cost adaptive NARMAX control (RCANC) to a command-following problem for uncertain Hammerstein systems. In particular, RCANC with ersatz nonlinearities is applied to linear systems cascaded with input nonlinearities. We assume that one Markov parameter of the linear plant is known. RCANC also uses knowledge of the monotonicity properties of the input no...

International Journal of Control Publication details, including instructions for authors and subscription information: http://www.informaworld.com/smpp/title~content=t713393989 Practical identification of NARMAX models using radial basis functions S. Chen a; S. A. Billings b; C. F. N. Cowan a; P. M. Grant a a Department of Electrical Engineering, University of Edinburgh, Edinburgh, Scotland, U....