Comparison between Artificial Neural Networks and Neurofuzzy Systems in Modeling and Control: a Case Study

This article presents a comparison of Artificial Neural Networks and NeuroFuzzy Systems applied for modeling and controlling a real system. The main objective is to control the temperature inside of a ceramics kiln. The details of all system components are described. The steps taken to arrive at the direct and inverse models using the two architectures: Adaptive Neuro Fuzzy Inference System and Feedfoward Neural Networks are described and compared. Finally, real time control results using Internal Model Control strategy are presented. Using available MATLAB software for both algorithms, the objective is to find which solution performs “better” comparing the performances of the solutions through different parameters for a specific case. Copyright © IFAC 2003