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 ...

In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the system based on neural network and time series analysis is proposed to deal one-month forecast produced power from photovoltaic modules (PVM). The PVM monocrystalline cell rated production 175 watts that placed at Heliopolis University, Bilbéis city, Egypt. NARX considered powerful enough emulate dynamic...

Foreign exchange rate prediction is a stimulating research area from past decade. There are several statistical and machine learning methods already have been proposed by the researchers for foreign exchange rate prediction which provide better results. These models performed a vital role in future financial decision making which is taken by financial department administration of that country a...

The NARX network is a dynamical neural architecture commonly used for inputoutput modeling of nonlinear dynamical systems. When applied to time series prediction, the NARX network is designed as a feedforward Time Delay Neural Network (TDNN), i.e. without the feedback loop of delayed outputs, reducing substantially its predictive performance. In this paper, we show that the original architectur...

Recurrent neural networks (RNNs) have shown success for many sequence-modeling tasks, but learning long-term dependencies from data remains difficult. This is often attributed to the vanishing gradient problem, which shows that gradient components relating a loss at time t to time t− τ tend to decay exponentially with τ . Long short-term memory (LSTM) and gated recurrent units (GRUs), the most ...

This chapter focuses on comparing the forecasting ability of the backpropagation neural network (BPNN) and the nonlinear autoregressive moving average with exogenous inputs (NARX) network trained with different algorithms; namely the quasi-Newton (Broyden-Fletcher-Goldfarb-Shanno, BFGS), conjugate gradient (Fletcher-Reeves update, Polak-Ribiére update, Powell-Beale restart), and Levenberg-Marqu...

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearitie...

Membrane receptors communicate between the external world and the cell interior. In bacteria, these receptors include the transmembrane sensor kinases, which control gene expression via their cognate response regulators, and chemoreceptors, which control the direction of flagellar rotation via the CheA kinase and CheY response regulator. Here, we show that a chimeric protein that joins the liga...

The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural n...