modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

This paper presents a methodology for identifying variable-structure models of magneto-rheological dampers (MRDs) that are structurally simple, easy to estimate and well suited for model-based control. Linear-in-the-parameters NARX models are adopted, and an identification method is developed based on the minimisation of the simulation error. Both the model structure and the parameters are sele...

â  abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...

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...

It has recently been shown that gradient descent learning algorithms for recurrent neural networks can perform poorly on tasks that involve long{term dependencies, i.e. those problems for which the desired output depends on inputs presented at times far in the past. In this paper we explore the long{term dependencies problem for a class of architectures called NARX recurrent neural networks, wh...

Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barom...

Regressor selection can be viewed as the rst step in the system identi cation process. The bene ts of nding good regressors before estimating complex models are especially clear for nonlinear systems, where the class of possible models is huge. In this article, a structured way of using the tool Analysis of Variance (ANOVA) is presented and used for NARX model (nonlinear autoregressive model wi...

This study aims at investigation of stimulation by using intra-spinal signals decoded from electrocorticography (ECoG) assessments to restore the movements of the leg in an animal model of spinal cord injury (SCI). The present work comprised of three steps. First, ECoG signals and the associated leg joint changes (hip, knee, and ankle) in sedated healthy rabbits were recorded in different trial...