Abstract Surrogate models play a vital role in overcoming the computational challenge designing and analyzing nonlinear dynamic systems, especially presence of uncertainty. This paper presents comparative study different surrogate modeling techniques for systems. Four methods, namely, Gaussian process (GP) regression, long short-term memory (LSTM) network, convolutional neural network (CNN) wit...

Previously, a nonlinear autoregressive network with exogenous input (NARX) demonstrated an excellent performance, far outperforming established method in optimal baseline subtraction, for defect detection guided wave signals. The principle is to train NARX on defect-free signals obtain filter that predicts the next point from previous points signal. trained then applied new measurement and outp...

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this setting, users only need supply series. Then, algorithm sets up appropriate features, estimate parameters in model, and calculate forecasts, without users’ intervention. The used include preprocessing, tests for tren...

Nutrients are essential to optimising plant growth. However, the introduction of fertiliser in a hydroponics setup influences pH level nutrient solution. This, turn, could affect plants' growth as many types plants require specific range grow optimally. Conventional cultivation performs adjustment manually – meticulous and error-prone process. Manual solutions is prone estimation errors, partic...

The management of irrigation main canals are studied in this research. One way improving is designing an efficient automatic control system the water that flows through canal pools, which usually carried out by PI controllers. However, since pools systems with large time delays and nonlinear hydrodynamics, these PIs tuned a very conservative so closed-loop instability may appear depending on ch...

  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...

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive filtering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data ...

Nonlinear empirical models are used in various applications. During model-building, five major steps usually have to be carried out: model structure selection, determination of input variables, complexity adjustment of the model, parameter estimation and model validation. These steps have to be repeated until a satisfactory model is found, which can be very time consuming and may require user i...

This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then us...