Nonlinear Process Identification using Neural Networks

In industry process control, the model identification of nonlinear systems are always difficult problems. The main aim of 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 exogenous input (NARMAX) model has been chosen in this study. The model is implemented by training a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with input-output experimental data is found and results shown that the neural model successfully predicts the evolution of the product composition. The simulation result illustrates the validity and feasibility of the nonlinear model identification. Trained data obtained from nonlinear process identification, can be used to control the nonlinear system.