A novel data reduction method for Takagi-Sugeno fuzzy system design based on statistical design of experiment

Applied Soft Computing Journal

Volumn 9, Issue 4, 2009, Pages 1367-1376

  Farzaneh, Yadollah ^a   Akbarzadeh Tootoonchi, Ali ^a  

^a FERDOWSI UNIVERSITY OF MASHHAD   (Iran)

Author keywords

Benchmark problems; Data reduction; Design of experiments; Factorial design; Simple design; T S system

Indexed keywords

BENCHMARK PROBLEMS; COMPUTATION TIME; EXISTING METHOD; FACTORIAL DESIGN; INVERTED PENDULUM; LEAST-SQUARE TECHNIQUES; NONLINEAR FUNCTIONS; SIMPLE DESIGN; SIMULATION RESULT; STATISTICAL DESIGN OF EXPERIMENTS; T-S SYSTEM; TAKAGI SUGENO FUZZY SYSTEMS; THREE-LEVEL FACTORIAL DESIGN; TRAINING DATA; TRAINING DATA SETS; TYPE SYSTEMS;

DESIGN OF EXPERIMENTS; FUZZY LOGIC; FUZZY SYSTEMS; MEMBERSHIP FUNCTIONS; PENDULUMS; STATISTICS;

DATA REDUCTION;

EID: 68849093646     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2009.06.005     Document Type: Article

References (38)

1. Takagi T., and Sugeno M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15 (1985) 116-132
2. Du H., and Zhang N. Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification. Appl. Soft Comput. 8 1 (2008) 676-686
3. Lien C.H., and Yu K.W. Robust control for Takagi-Sugeno fuzzy systems with time-varying state and input delays. Chaos Soliton Fract. 35 5 (2008) 1003-1008
4. Luo Y.B., Cao Y.Y., and Sun Y.X. Robust stability of uncertain Takagi-Sugeno fuzzy systems with time-varying input-delay. Acta Automatica Sinica 34 1 (2008) 87-92
5. Hong-Rui W., Lei L., and Zi-Hui Z. Real-time online fuzzy modeling for robotic manipulators. International Conference on Machine Learning and Cybernetics 1 (2007) 477-481
6. Shakouri H., Nadimi G.R., and Ghaderi F. A hybrid TSK-FR model to study short-term variations of the electricity demand versus the temperature changes. Expert Syst. Appl. 36 (2009) 1765-1772
7. Babu R., and Verbruggen H. Neuro-fuzzy methods for nonlinear system identification. Annu. Rev. Control 27 1 (2003) 73-85
8. Yen J., Langari R., and Zadeh L.A. Industrial Applications of Fuzzy Control and Intelligent Systems (1995), IEEE Press, New York
9. Wongsuwarn H., and Laowattana D. Neuro-fuzzy algorithm for a biped robotic system. Int. J. Appl. Math. Comput. Sci. 3 4 (2006)
10. Katić D., and Vukobratović M. Survey of intelligent control techniques for humanoid robots. J. Intell. Robot. Syst. 37 (2003) 117-141
11. Ari E.O., Erkmen I., and Erkmen A.M. A FACL controller architecture for a grasping snake robot. IEEE/RSJ International Conference on Intelligent Robots and Systems (2005) 1748-1753
12. Seung Y., Shin D., Kim J.Y., Baek S., and Min S.H. Obstacle recognition and collision avoidance of a fish robot based on fuzzy neural networks. ICFIE2007 Conference (2007)
13. Feng G. A survey on analysis and design of model-based fuzzy control systems. IEEE Trans. Fuzzy Syst. 14 5 (2006) 676-697
14. Seng T.L., Khalid M.B., and Yusof R. Tuning of a neuro-fuzzy controller by genetic algorithm. IEEE Trans. Syst. Man Cybern. B 29 (1999) 226-236
15. Wang L., and Langari R. Complex systems modeling via fuzzy logic. IEEE Trans. Syst. Man Cybern. 26 (1996) 100-106
16. Jang J. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23 (1993) 665-685
17. Yen J., Wang L., and Gillepie C.W. Improving the interpretability of TSK fuzzy models by combining global learning and local learning. IEEE Trans. Fuzzy Syst. 6 4 (1998) 530-537
18. Jin Y. Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 8 2 (2000) 212-221
19. Chen M.Y., and Linkens D.A. Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets Syst. 142 2 (2004) 243-265
20. Ouyang C.S., Lee W.J., and Lee S.J. A TSK-type neuro fuzzy network approach to system modeling problems. IEEE Trans. Syst. Man Cybern. B: Cybern. 35 4 (2005) 751-767
21. Montgomery D.C. Design and Analysis of Experiments. 6th ed. (2005), Wiley
22. Takeiti C.Y., Kieckbusch T.G., and Collares-Queiroz F.P. Optimization of the jet steam instantizing process of commercial maltodextrins powders. J. Food Eng. 86 3 (2008) 444-452
23. Tang J.B., Yang H.M., Song S.L., Zhu P., and Ji A.G. Effect of glycine and Triton X-100 on secretion and expression of ZZ-EGFP fusion protein. Food Chem. 108 2 (2008) 657-662
24. Jalbani N., Kazi T.G., Arain B.M., Jamali M.K., Afridi H.I., and Sarfraz R.A. Application of factorial design in optimization of ultrasonic-assisted extraction of aluminum in juices and soft drinks. Talanta 70 2 (2006) 307-314
25. Myers R.M., and Montgomery D.C. Response Surface Methodology. 2nd ed. (2002), Wiley, New York
26. Hedayat A.S., Solane N.J.A., and Stufken J. Orthogonal Arrays: Theory and Applications (1999), Springer-Verlag, New York
27. Landry M., Campbell S.A., Morris K., and Aguilar C.O. Dynamics of an inverted pendulum with delayed feedback control. SIAM J. Appl. Dyn. Syst. 4 2 (2005) 333-351
28. Wong C.C., and Her S.M. A self-generating method for fuzzy system design. Fuzzy Sets Syst. 103 (1999) 13-25
29. Roopaei M., Zolghadri M., and Meshksar S. Enhanced adaptive fuzzy sliding mode control for uncertain nonlinear systems. Commun. Nonlinear Sci. Numer. Simulat. 14 (2009) 3670-3681
30. Chen P.C., Chen C.W., and Chiang W.L. GA-based fuzzy sliding mode controller for nonlinear systems. Mathematical Problems in Engineering (2008) art. no. 325859
31. Sun Y.L., and Er M.J. Hybrid fuzzy control of robotics systems. IEEE Trans. Fuzzy Syst. 12 6 (2004) 755-765
32. Lin Y., Cunningham G.A., and Coggeshall S.V. Using fuzzy partitions to create fuzzy systems from input-output data and set the initial weights in a fuzzy neural network. IEEE Trans. Fuzzy Syst. 5 4 (1997) 614-621
33. Gonzalez J., Rojas I., Pomares H., Ortega J., and Prieto A. A new clustering technique for function approximation. IEEE Trans. Neural Networks 13 1 (2002) 132-142
34. Juang C.F. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans. Fuzzy Syst. 10 2 (2002) 155-170
35. Lee S.J., and Ouyang C.S. A neurofuzzy system modeling with self-constructing rule generation and hybrid SVD-based learning. IEEE Trans. Fuzzy Syst. 11 (2003) 341-353
36. Setnes M., Babuška R., Kaymak U., and Lemke H.R.v.N. Similarity measures in fuzzy rules base simplification. IEEE Trans. Syst. Man Cybern. B: Cybern. B 28 3 (1998) 376-386
37. Rezaee B., and Fazel Zarandi M.H. A new approach For TSK-type fuzzy model design. Fifth International Conference on Fuzzy Systems and Knowledge Discovery. IEEE Computer Society (2008) 433-437
38. Hurmuzlu Y., Genot F., and Brogliato B. Modeling, stability and control of biped robots-a general framework. Automatica 40 (2004) 1647-1664