Gait event detection is important for diagnosis and evaluation. This is a challenging endeavor due to subjectivity, high amount of data, among other problems. ANFIS (Artificial Neural Fuzzy Inference Systems), ARX (Autoregressive Models with Exogenous Variables), OE (Output Error models), NARX (Nonlinear Autoregressive Models with Exogenous Variables) and models based on NN (neural networks) we...

NarX-NarL and NarQ-NarP are paralogous two-component regulatory systems that control Escherichia coli gene expression in response to the respiratory oxidants nitrate and nitrite. Nitrate stimulates the autophosphorylation rates of the NarX and NarQ sensors, which then phosphorylate the response regulators NarL and NarP to activate and repress target operon transcription. Here, we investigated b...

Anaerobic respiratory gene expression in Escherichia coli is differentially controlled by nitrate and nitrite through dual interacting two-component regulatory systems. The NarX sensor is one of two membrane-spanning sensor kinases that control the phosphorylation state of two DNA-binding response regulators. We have studied NarX autophosphorylation in crude membrane preparations from cells tha...

Recently, fully connected recurrent neural networks have been proven to be computationally rich—at least as powerful as Turing machines. This work focuses on another network which is popular in control applications and has been found to be very effective at learning a variety of problems. These networks are based upon Nonlinear AutoRegressive models with eXogenous Inputs (NARX models), and are ...

Siek, M. and Solomatine, D.P., 2011. Real-time data assimilation for chaotic storm surge model using NARX neural network. Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium), 1189 – 1194. Szczecin, Poland, ISSN 0749-0208 This paper introduces a real-time data assimilation technique where Nonlinear AutoRegressive with eXogenous inputs (NARX) neural networ...

In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the inputs, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of influence of the parti...

In practice, it is usually difficult to obtain the physical model of nonlinear, rotor-bearing systems due to uncertain nonlinearities. In order to solve this issue to conduct the analysis and design of nonlinear, rotor-bearing systems, in this study, a data driven NARX (Nonlinear Auto-Regressive with exogenous inputs) model is identified. Due to the lack of the random input signal which is requ...

This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) and autoregressive moving average (ARMA) for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machi...

The aim of the study is to find right architecture NARX neural network, in order perform daily prediction maximum wind speed Laayoune city. We relied on Levenberg-Marquardt optimization algorithm. RMSE error metric showed that NARX-SP outperforms NARX-P.

we have that Bayesian regression, a nonparametric identification technique with several appealing features, can be applied to the identification of NARX (nonlinear ARX) models. However, its computational complexity scales as O(N 3) where N is the data set size. In order to reduce complexity, the challenge is to obtain fixed-order parametric models capable of approximating accurately the nonpara...