Foreign exchange market is one of the most complex dynamic market with high volatility, non linear and irregularity. As the globalization spread to the world, exchange rates forecasting become more important and complicated. Many external factors influence its volatility. To forecast the exchange rates, those external variables can be used and usually chosen based on the correlation to the pred...

This paper provides a formulation for using the delta-operator in the modelling of non-linear systems. It is shown that a unique representation of a deterministic non-linear auto-regressive with exogenous input (NARX) model can be obtained for polynomial basis functions using the delta-operator and expressions are derived to convert between the shiftand deltadomain. A delta-NARX model is applie...

This paper describes an approach to predict non-verbal cues from speech-related features. Our previous investigations of audiovisual speech showed that there are strong correlations between the two modalities. In this work we developed two models using different kinds of Recurrent Artificial Neural Networks: Elman and NARX, to predict parameters of activity for head motion using linguistic and ...

Model identification of polynomial NARX models involves a lengthy and computationally intensive procedure for selecting the model structure among a possibly large set of candidate regressors. If the model structure is under-parameterized to reduce the burden of the model selection phase, unsatisfactory results are generally obtained. This inaccuracy problem can be somewhat circumvented by focus...

algorithm [3]. A method is presented for calculating the Higher-order In this paper, the method of Way and Green [2] will Frequency Response Functions (HFRFs) of NARX neural be extended to the frequency-domain. By harmonically networks. HFRFs are the Fourier transforms of Volterra probing the network equation, the HFRFs of the network kernels and can be viewed as multi-dimensional equivawill be...

Learning long-term temporal dependencies with recurrent neural networks can be a difficult problem. It has recently been shown that a class of recurrent neural networks called NARX networks perform much better than conventional recurrent neural networks for learning certain simple long-term dependency problems. The intuitive explanation for this behavior is that the output memories of a NARX ne...

Weather forecasting is a challenging time series forecasting problem because of its dynamic, continuous, data-intensive, chaotic and irregular behavior. At present, enormous time series forecasting techniques exist and are widely adapted. However, competitive research is still going on to improve the methods and techniques for accurate forecasting. This research article presents the time series...

Bayesian nonlinear system identification for one of the major classes of dynamic model, the nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied to date. Markov chain Monte Carlo (MCMC) methods have been developed, which tend to be accurate but can also be slow to converge. In this contribution, we present a novel, computationally efficient solution to sparse ...

--------------------------------------------------ABSTRACT-------------------------------------------------------This study aims to investigate suitable model and forecast future wheat price using backpropagation neural network (BPNN) and nonlinear autoregressive models with exogenous inputs (NARX) networks. The price of wheat was estimated using prices of 3 types of grains widely used in agric...

An exact classification of different gait phases is essential to enable the control of exoskeleton robots and detect the intentions of users. We propose a gait phase classification method based on neural networks using sensor signals from lower limb exoskeleton robots. In such robots, foot sensors with force sensing registers are commonly used to classify gait phases. We describe classifiers th...