This section presents, an important class of multivariate non-linear models for discrete time: NARX (Nonlinear AutoRegressive with eXogenous inputs) feedforward NN [2]. The use of NARX Networks for forecasting has gained popularity due to its capacity to represent nonlinear systems with good precision and due to the fact that any data which can, in some way, supply information to the series of ...

This paper presents a continuous-time recurrent neural-network model for nonlinear optimization with any continuously differentiable objective function and bound constraints. Quadratic optimization with bound constraints is a special problem which can be solved by the recurrent neural network. The proposed recurrent neural network has the following characteristics. 1) It is regular in the sense...

A damage detection approach is developed using nonlinear autoregressive with exogenous inputs (NARX) neural networks and a statistical inference technique. Within a large spatially extended dynamic system, an instrumented local substructure may be represented by a neural network, to predict the dynamic response of a given sensor from that of its neighbors. Without change in the system propertie...

The applicability of approximate NARX models of non-linear dynamic systems is discussed. The models are obtained by a new version of Fourier analysis-based neural network also described in the paper. This constitutes a reformulation of a known method in a recursive manner, i.e. adapted to account for incoming data on-line. The method allows us to obtain an approximate model of the non-linear sy...

A recurrent neural network is studied in this paper. A multi–context–recurrent neural network is defined and trained with back propagation, and is then applied to the short–term energy load forecasting task. The idea is to predict a daily maximum load for an arbitrary month ahead. A multi–context–recurrent neural network model was simulated and trained with different training sets to predict th...

New technologies have been developed and adopted to generate energy from renewable sources satisfy the increasing demand without causing environmental damage. However, estimating power output of inherently intermittent, weather-driven, non-dispatchable is a major scientific societal concern. In this study, neural network model enable short-to-middle term forecasts photovoltaic (PV) system provi...

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asy...