Special Issue Article Open Access

Recurrent Neural Network Identification: Comparative Study on Nonlinear Process

Abstract

Neural networks (NNs) have been successfully applied to solve a variety of application problems including nonlinear modelling and identification. The main contribution of this paper is modeling and identification of pH process based on recurrent neural networks. The most powerful types of neural network-based nonlinear autoregressive models, namely, Neural Network Auto-Regressive Moving Average with eXogenous input models (NNARMAX), Neural Network Output Error Models (NNOE) and Neural Network Auto-Regressive model with eXogenous inputs models (NNARX) will be applied comparatively of the pH process identification. Moreover, the evaluation of different nonlinear Neural Network Auto-Regressive models of pH process with various hidden layer nodes is completely discussed. On this basis the features of each identified model of the highly nonlinear pH process have been analyzed and compared. The performance analysis shows that the nonlinear NNARX model yields more performance and higher accuracy than the other nonlinear NNARMAX and NNOE model schemes. The proposed method to identification is not only of the pH process but also of other nonlinear and time-varied parametriic industrial systems.

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