A Simplified Model Structure for an Activated Sludge System

Gaya, Muhammad Sani; Wahab, Norhaliza Abdul; Sam, Yahaya Md; Anuar, Aznah Nor; Samsuddin, Sharatul Izah; Razali, Mashitah Che

doi:10.21307/ijssis-2017-585

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2014

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“…ARX is based on input, output and transfer characteristics without adequate knowledge of internal operation and structure of a system under consideration [12]. ARX approach results in solving linear regression equations in an analytical form.…”

Section: Arxmentioning

confidence: 99%

Comparison of NARX Neural Network and Classical Modelling Approaches

Gaya

Wahab

Sam

et al. 2014

AMM

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Classical optimization tools are effective when precise mechanistic models exist to support their design and implementation. However, most of the real-world processes are complex due to either nonlinearities or uncertainties (or both) and environmental variations, thus making realizing accurate mathematical models for such processes quite difficult or often impossible. Black box approach tends to present a better alternative in such situations. This paper presents a comparison of nonlinear autoregressive with eXogenous (NARX) neural network and traditional modelling techniques [autoregressive with exogenous input (ARX) and autoregressive moving average with exogenous input (ARMAX)]. The models were validated using experimental data from full-scale plants. Simulation results revealed that the performance of the NARX neural network is better compared to the ARMAX and ARX. The NARX neural network may serve as a valuable forecasting tool for the plants.

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Section: Arxmentioning

confidence: 99%