This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS)...

Identi cation results for the shaft-speed dynamics of an aircraft gas turbine, under normal operation, are presented. As it has been found that the dynamics vary with the operating point, nonlinear models are employed. Two di7erent approaches are considered: NARX models, and neural network models, namely multilayer perceptrons, radial basis function networks and B-spline networks. A special att...

This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, t...

The hysteretic nonlinear dependence of pre-sliding friction force on displacement is modeled using different physics-based and black-box approaches including various Maxwell-slip models, NARX models, neural networks, nonparametric (local) models and dynamical networks. The efficiency and accuracy of these identification methods is compared for an experimental time series where the observed fric...

With the development of design technology, more and advanced diverse wind tunnels have been constructed to match complex requirements. However, it is hard a precise physical model tunnel that can be controlled. In addition, if new designed, experimental data may insufficient build controlling model. This article reports research on following two models: (1) for 0.6 m continuous transonic suppor...

We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling values and an upper boundary condition scaling factor (BF). The model utilizes feedback delay 2 time steps (i.e., 5 min steps) the most efficient number hidden...

Abstract. Reservoirs play a key role in many human societies due to their capability manage water resources. In addition supply and hydropower production, ability retain control the flow makes them valuable asset for flood mitigation. This is function, since extreme events have increased last few decades as result of climate change, therefore, application mechanisms capable mitigating damage wi...