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...

Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG), as a...

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...

great effect of three way catalytic convertor (twc) performance on oxygen sensor output voltage has made the sensor (located after catalyst) as the main signal in almost all today’s twc monitoring algorithms. in this paper output voltage of nonlinear oxygen sensor is estimated using a nonlinear autoregressive with exogenous inputs (narx) model. the estimation uses ecu calculated exhaust gas flo...

Chaotic time-series is a dynamic nonlinear system whose features can not be fully reflected by Linear Regression Model or Static Neural Network. While Nonlinear Autoregressive with eXogenous input includes feedback of network output, therefore, it can better reflect the system’s dynamic feature. Take annual active times of sunspot as an example, after verifying the chaos of sunspot time-series ...

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...

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...

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...

The Nar two-component regulatory system, consisting of the dual sensor-transmitters NarX and NarQ and the dual response regulators NarL and NarP, controls the expression of various anaerobic respiratory pathway genes and fermentation pathway genes. Although both NarX and NarQ are known to detect the two environmental signals nitrate and nitrite, little is known regarding the sensitivity and sel...