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...

Nonlinear autoregressive moving average with exogenous inputs (NARMAX) models have been successfully demonstrated for modeling the input-output behavior of many complex systems. This paper deals with the proposition of a scheme to provide time series prediction. The approach is based on a recurrent NARX model obtained by linear combination of a recurrent neural network (RNN) output and the real...

The problem of chaotic time series prediction is studied in various disciplines now including engineering, medical and econometric applications. Chaotic time series are the output of a deterministic system with positive Liapunov exponent. A time series prediction is a suitable application for a neuronal network predictor. The NN approach to time series prediction is non-parametric, in the sense...