This paper presents a performance of Neural Network Autoregressive with Exogenous Input (NNARX) model structure and evaluates the training data that provides robust model on fresh data set, using neural network type of back-propagation known as multilayer perceptron (MPP). The plant under test is a heat exchanger process control training system called QAD Model BDT 921. A real input-output data has been collected and will be used to identify the plant. The model was estimated by prediction error method with Levenberg-Marquardt algorithm for training neural networks. It is expected that the training data covering the full operating condition will be the optimum training data. The model was validated by residual analysis and model fit. It will be presented and concluded. The simulation results show that the identification is able to identify plant’s good model. This identification can be used to design the plant controller and improve its performance.

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