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...

The complex interactions between vapour-liquid equilibrium, mass and energy transports and reaction kinetics in reactive distillation (RD) process have constituted serious computational challenges to its modelling and control. This is because large numbers of nonlinear differential algebraic equations are needed to represent the process when using first principle modelling approach. This work d...

Mycobacterium tuberculosis is one of the strongest reducers of nitrate in the genus Mycobacterium: Under microaerobic conditions, whole cells exhibit upregulation of activity, producing approximately eightfold more nitrite than those of aerobic cultures of the same age. Assays of cell extracts from aerobic cultures and hypoxic cultures yielded comparable nitrate reductase activities. Mycobacter...

Tide variations are affected not only by periodic movement of celestial bodies but also time-varying interference from the external environment. To improve accuracy tide prediction, a modular level prediction model (HA-NARX) is proposed. This divides data into two parts: astronomical tide-generating forces and nonastronomical various environmental factors. Final results obtained using nonlinear...

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...

The present study investigates the prediction efficiency of nonlinear system-identification models, in assessing the behavior of a coupled structure-passive vibration controller. Two system-identification models, including Nonlinear AutoRegresive with eXogenous inputs (NARX) and adaptive neuro-fuzzy inference system (ANFIS), are used to model the behavior of an experimentally scaled three-story...

The paper presents the application of support vector regression (SVR) to accurate forecasting of the tangential displacement of a concrete dam. The SVR nonlinear autoregressive model with exogenous inputs (NARX) was developed and tested using experimental data collected during fourteen years. A total of 573 data were used for training of the SVR model whereas the remaining 156 data were used to...

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...

This study examines modeling and simulation of the transient thermal behavior of a solar collector adsorber tube. The data used for model setup and validation were taken experimentally during the start-up procedure of a solar collector adsorber tube. ANN models are developed based on the nonlinear autoregressive with exogenous input NARX model and are implemented using the MATLAB® tools includi...

* corresponding author: [email protected] Abstract This paper presents a comparison study between radial basis function neural network (RBFNN), feed forward multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy (ANFIS) technique to model the activated sludge process (ASP). All of these techniques are based on the nonlinear autoregressive with eXogenous input (NARX) structure. The...