Neuro-fuzzy methods for modeling and identification

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.

[1]  Martin Brown,et al.  Neurofuzzy adaptive modelling and control , 1994 .

[2]  Tor Arne Johansen,et al.  Operating regime based process modeling and identification , 1997 .

[3]  Kazuo Tanaka,et al.  Fuzzy regulators and fuzzy observers: relaxed stability conditions and LMI-based designs , 1998, IEEE Trans. Fuzzy Syst..

[4]  M. Setnes,et al.  Comparision of Two Construction Algorithms for Takagi-Sugeno Fuzzy Models , 1999 .

[5]  Dimiter Driankov,et al.  Fuzzy model identification - selected approaches , 1997 .

[6]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[7]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[8]  J. Abonyi,et al.  Local and global identification and interpretation of parameters in Takagi-Sugeno fuzzy models , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[9]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[10]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[11]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[12]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[13]  Dale E. Seborg,et al.  Modelling and Self-Tuning Control of a Multivariable pH Neutralization Process Part I: Modelling and Multiloop Control , 1989, 1989 American Control Conference.

[14]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..