Neuro-fuzzy methods for nonlinear system identification

✍ Scribed by Robert Babuška; Henk Verbruggen

• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 263 KB
• Volume: 27
• Category: Article
• ISSN: 1367-5788
• DOI: 10.1016/s1367-5788(03)00009-9

✦ Synopsis

Most 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. Neuro-fuzzy 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. In this paper, an overview of neuro-fuzzy modeling methods for nonlinear system identification is given, with an emphasis on the tradeoff between accuracy and interpretability.