Nonlinear Process Identification using Neural Networks

Provided by: International Journal of Engineering Sciences & Research Technology (IJESRT)
Topic: Networking
Format: PDF
In industry process control, the model identification of nonlinear systems are always difficult problems. This paper is to establish a reliable model for the nonlinear process. In many applications, development of empirical nonlinear model from dynamic plant data. This process is known as 'Nonlinear system identification'. Artificial neural networks are the most popular frame-work for empirical model development. In order to obtain this reliable model for the process dynamics, the neural black-box identification by means of a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) model has been chosen in this paper.

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