Recurrent neural networks have become popular models for system identiication and time series prediction. NARX (Nonlinear AutoRegressive models with eXogenous inputs) neural network models are a popular subclass of recurrent networks and have been used in many applications. Though embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We show ...

A neural network based-approach for structural health monitoring was presented. The proposed approach involves two steps. The first step, system identification, uses NARX (Non-linear Auto-Regressive with eXogenous) neural networks to identify the undamaged and damaged states of a structural system. The second step, structural damage detection, uses the aforementioned trained NARX neural network...

Recurrent neural networks have become popular models for system identification and time series prediction. Nonlinear autoregressive models with exogenous inputs (NARX) neural network models are a popular subclass of recurrent networks and have been used in many applications. Although embedded memory can be found in all recurrent network models, it is particularly prominent in NARX models. We sh...

It has previously 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. We show that the long-term dependencies problem is lessened for a class of architectures called nonlinear autoregressive models ...

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. We show that the long-term dependencies problem is lessened for a class of architectures called Nonlinear AutoRegressive models w...

Intelligent assessment of information gathered from industrial-grade data loggers for preemptive maintenance is one of the foremost areas of research in conditional monitoring. Due to the general operating environment, there exists a non-linear relationship between the input and output data gathered from these sensors. Moreover, the transmission of data from such dynamic environments is general...

A new unified modelling framework based on the superposition of additive submodels, functional components, and wavelet decompositions is proposed for nonlinear system identification. A nonlinear model, which is often represented using a multivariate nonlinear function, is initially decomposed into a number of functional components via the well known analysis of variance (ANOVA) expression, whic...

Aiming at engine health management, a novel hybrid prediction method is proposed for exhaust gas temperature (EGT) of turbine engines. This model combines nonlinear autoregressive with exogenous input (NARX) and moving average (MA) model. A feature attention mechanism-enhanced long short-term memory network (FAE-LSTM) first developed to construct the NARX model, which used identifying aircraft ...