Neuro-fuzzy methods for modeling and identification

Authors:
Robert Babuška
Affiliations:
Delft University of Technology, Faculty of Information Technology and Systems, Control Systems Engineering Group, P.O. Box 5031, 2600 GA Delft, The Netherlands
Venue:
Recent advances in intelligent paradigms and applications
Year:
2003

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Abstract

Modern processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but also in industrial applications. Neurofuzzy modeling can be regarded as a gray-box technique on the boundary between neural networks and qualitative fuzzy models. The tools for building neuro-fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. This chapter addresses the use of neuro-fuzzy models in system identification.