Nonlinear black box modelling - Fuzzy networks versus neural networks

Neural Computing and Applications

Volumn 8, Issue 2, 1999, Pages 151-162

  Bernd, T ^a   Kleutges, M ^a   Kroll, A ^b  

^a UNIVERSITY OF DUISBURG ESSEN   (Germany)

^b ABB CORPORATE RESEARCH   (Germany)

Author keywords

Flexible robot; Fuzzy networks; Hydraulic drive; Identification; Neural networks; Pneumatic drive

Indexed keywords

EID: 0033244192     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s005210050017     Document Type: Article

References (24)

1. Sjöberg J, Zhang Q, Ljung L, Benveniste A, Delyon B, Glorennec P-Y, Hjalmarsson H, Juditsky A. Nonlinear black-box modeling in system identification: a unified overview. Automatica 1995; 31(12): 1691-1724
2. Pedrycz W. An identification algorithm in fuzzy relational systems. Fuzzy Sets and Systems 1984; 13: 153-167
3. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 1985; 15: 116-132
4. Kroll A. Identification of functional fuzzy models using multidimensional reference fuzzy sets. Fuzzy Sets and Systems 1996; 80: 149-158
5. Pickhardt R. Adaptive control using a multi-model method, PhD thesis (in German). University of Bochum, VDI Fortschrittberichte Reihe 8 Nr. 499. Düsseldorf: VDI-Verlag, 1995
6. Shamma JS, Athans M. Gain scheduling: potential hazards and possible remedies. IEEE Control Systems 1992; 101-107
7. Jang JSR, Sun C-T, Mitzutani E. Neuro-Fuzzy and Soft Computing. Upper Saddle River: Prentice Hall, 1997
8. Su HT, McAvoy TJ. Artificial neural networks for nonlinear process identification and control. In: M Henson, D-E Seborg (eds), Nonlinear Process Control, Englewood Cliffs, NJ: Prentice Hall, 1996, pp 371-428
9. Nelles O, Ernst S, Isermann R. Neural networks for identification of nonlinear, dynamical systems: an overview. 1997; 6/97: 251-262 (in German)
10. Bezdek JC. Pattern Recognition with Fuzzy Objective Function Algorithms, New York, Plenum Press, 1981
11. Bernd T, Kroll A, Schwarz H. LS-optimal fuzzy-modelling and its application to pneumatic drives. ECC 97, Brussels, 1997, 1.-4.7.1997
12. Sjöberg J, Ljung L. Overtraining, Regularisation and Searching for Minimum in Neural Networks. Technical Report, Department of Electrical Engineering, Linköping University, Sweden, 1992
13. Rumelhart DE, McClelland JL. Learning Internal Representation by Error Propagation. In: Parallel Distributed Processing, vol. 1, Chapter 8. Cambride, MA: MIT Press, 1986, pp 318-362
14. Mølier MF. A scaled conjugated gradient algorithm for fast supervised learning. Neural Networks 1993; 6(4): 525-533
15. Dennis Jr JE, Schnabel RB. Numerical Methods for Unconstrained Optimisation and Nonlinear Equations. Englewood Cliffs, NJ: Prentice Hall, 1983
16. Moré JJ. The Levenberg-Marquardt algorithm: implementation and theory. In: GA Watson (ed), Numerical Analysis, Lecture Notes in Mathematics 630. Berlin: Springer-Verlag, 1978, pp 105-116
17. Levenberg K. A method for the solution of certainnonlinear problems in least-squares. Quart Appl Math 1944; 2: 164-168
18. Marquardt DW. An algorithm for least-squares estimation of nonlinear parameters. J Soc Indust Appl Math 1963; 11: 413-441
19. Zell A. Simulation Neuronaler Netze. Addison-Wesley, 1994 (in German)
20. Sugeno M, Yasukawa T. A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems 1993; 1: 7-31
21. Jang J-SR. ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics 1993; 23: 665-685
22. Frik M, Kleutges M. Approximation of elastic deformations of robot arms using neural networks. ZAMM: Zeitschrift für angewandte Mathematik und Mechanik, Band 77, Supplement 2, Akademie-Verlag, pp S91-S92 (in German)
23. Kroll A, Agte A. Modelling hydraulic drives using fuzzy models. O + P 1997: 414-416
24. Tanaka K, Sugeno M. Stability analysis and design of fuzzy control systems. Fuzzy Sets and Systems 1992; 45: 135-156