This paper provides a comparison of techniques used to model the fuel flow to shaft speed relationship of a Spey gas turbine engine. Linear models are examined and the need for nonlinear modelling is justified. A technique based on nonparametric data analysis is proposed, to simplify the identification of a nonlinear model of the engine. A NARMAX model is identified and its performance validate...

This study presents the estimation of a nonlinear autoregressive moving average with exogenous inputs (NARMAX) model of a novel hydraulically actuated electronic unit injection (HEUI) system. The injection pressure-fuel rate relationship is detected to understand the HEUI system and its effects on engine performance. The dynamics of causation is first investigated in the time domain to estimate...

The problem of constructing data-based, predictive, reduced models for the KuramotoSivashinsky equation is considered, under circumstances where one has observation data only for a small subset of the dynamical variables. Accurate prediction is achieved by developing a discrete-time stochastic reduced system, based on a NARMAX (Nonlinear Autoregressive Moving Average with eXogenous input) repre...

In this paper a neural approach to distillation columns modelling is described. In particular a Debutanizer colums is considered and a real-time estimate of the butane percentage (C4) in the bottom draw (C5) is obtained by a NARMAX model implemented with a Multi-Layer Perceptron. The analyser of the C4 in C5 percentage used at present, provides a measure after a great and unknown delay, and is ...

In this paper two nonlinear modelling approaches are employed to derive single nonlinear models for a Rolls Royce aircraft gas turbine. The first approach is based on the estimation of a NARMAX model using conventional structure selection and parameter estimation techniques, and the second approach is based on the use of feedforward Multilayer-Perceptron (MLP) neural networks. The performances ...

This paper proposes system identification on application of nonlinear AR (NAR) based on Artificial Neural Network (ANN) for monitor of dengue infections. In building the model, three selection criteria, i.e. the final prediction error (FPE), Akaike’s Information Criteria (AIC), and Lipschitz number were used. Each of the models is divided into two approaches, which are unregularized approach an...

Fuzzy Inductive Reasoning (FIR) is a qualitative inductive modeling and simulation methodology for dealing with dynamical systems. It has proven to be a powerful tool for qualitative model identi cation and prediction of future behavior of various kinds of dynamical systems, especially from the soft sciences, such as biology, biomedicine, and ecology. This paper focuses on modeling aspects of t...

Adaptation is a hallmark of sensory processing. We studied neural adaptation in intracellular voltage responses of the R1-R6 photoreceptors, of the fruit fly Drosophila, subjected to light patterns of naturalistic distribution at varying intensity levels. We use experimental data in a stepwise empirical modelling procedure to estimate a non-linear dynamical model (NARMAX) with variable gain. Th...