To understand changes in ecosystems, the appropriate scale at which to study them must be determined. Large marine ecosystems (LMEs) cover thousands of square kilometres and are a useful classification scheme for ecosystem monitoring and assessment. However, averaging across LMEs may obscure intricate dynamics within. The purpose of this study is to mathematically determine local and regional p...

This paper examines the estimation of a global nonlinear gas turbine model using NARMAX techniques. Linear models estimated on small-signal data are first examined and the need for a global nonlinear model is established. A nonparametric analysis of the engine nonlinearity is then performed in the time and frequency domains. The information obtained from the linear modelling and nonlinear analy...

This paper describes a genetic programming approach for applications on prediction of real meteorological data. The well-know SISO NARMAX model is used to model and forecast this real time series. The evaluation of candidate models is based on a set of criteria taken into consideration in order to get the simpler and more accurate model for prediction.

Fundamental sensor-motor couplings form the backbone of most mobile robot control tasks, and often need to be implemented fast, efficiently and nevertheless reliably. Machine learning techniques are therefore often used to obtain the desired sensor-motor competences. In this paper we present an alternative to established machine learning methods such as artificial neural networks, that is very ...

Electricity price prediction through statistical and machine learning techniques captures market trends would be a useful tool for energy traders to observe fluctuations increase their profits over time. A Nonlinear AutoRegressive Moving Average model with eXogenous inputs (NARMAX) identifies key energy-related factors that influence hourly electricity modelling. We propose use transparent NARM...

This paper presents an integral minimum variance-like controller design based upon a Constant Coefficient Pooled Nonlinear AutoRegressive Moving Average with eXogenous excitation (CCP-NARMAX) representation. The use of pooling techniques significantly enhances the NARMAX representation’s ability to accurately describe systems performing under various operating conditions such as aircraft system...

Multi-input single-output (MISO) nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been derived to forecast the > 0.8 MeV and > 2 MeV electron fluxes at geostationary Earth orbit (GEO). The NARMAX algorithm is able to identify mathematical model for a wide class of nonlinear systems from input–output data. The models employ solar wind parameters as inputs to pro...