Buradasınız

Anasayfa » Content

GENERALIZED MODELING PRINCIPLES OF A NONLINEAR SYSTEM WITH A DYNAMIC FUZZY NETWORK

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2003

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Generalized modeling principles of a nonlinear system with a dynamic fuzzy network (DFN)- a network with unconstrained connectivity and with dynamic fuzzy processing units called ‘feurons’, have been given. DFN model has been trained both in open loop and closed loop forms to satisfy these principles. Several system trajectories with a PRBS input have been used for open loop training. DFN model obtained from open loop training was used in a closed loop training with an extended Kalman filter (EKF) in an observer design. For gradient computations adjoint sensitivity method has been used.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-generalized-modeling-principles-nonlinear-system-dynamic-fuzzy-network.pdf
  • 1
727-734
  • Turkish

REFERENCES

References: 

[1] Aguirre L. A., “Open-loop Model Matching in Frequency Domain”, Electronics Letters, vol. 28, pp 484-485, 1992
Generalized Modeling Principles Of A Nonlinear System With A Dynamic Fuzzy Network
734
A.Ferit KONAR, Yusuf OYSAL
[2] Bryson A. E., and Ho Y. C., Applied Optimal Control. Hemisphere, 1975.
[3] Burton T. D., Introduction to Dynamic Systems Analysis, McGraw-Hill, Inc., 1994
[4] Edgar T. F., and Himmelblau D. H., Optimization of Chemical Processes. McGraw-Hill, 1988.
[5] Gevers M., Codrons B., De Bruyne F., “Model Validation in Closed-loop”, Proc. Of the 1999 American Control Conference, 326-330, San Diego, CA, USA,1999
[6] Jordan M. I., “Attractor dynamics and parallelism in connectionist sequential machine.’’ Proc. of the Eighth Annual Conference of Cognitive Science Society. Hillsdale, N.J.: Lawrence Erlbaum Associates, 1986.
[7] Khalil H. K., Nonlinear Systems, Prentice-Hall, NJ, 1996
[8] Konar A. F., et al, XIMKON: an expert simulation and control program. In Artificial Intelligence and Process Engineering. New York: Academic Press, 1990.
[9] Konar A. F., and Samad T., “Hybrid Neural Network/ Algorithmic System Identification.’’ Technical Report SSDC-91-I4051-1, Honeywell SSDC, 3660 Technology Drive, Minneapolis, MN 55418, 1991.
[10] Konar A. F., and Samad T., “Dynamic Neural Networks.’’ Technical report SSDC-92-I 4142-2, Honeywell Technology Center, 3660 Technology Drive, Minneapolis, MN 55418, 1992.
[11] Konar A. F., and Samad T., “Philosophical Aspects of Intelligent Control,” IEEE Int. Symp. On Intelligent Control (ISIC’97), İstanbul 1997
[12] Konar A. F., Becerikli Y. and Samad T., “Trajectory Tracking With Dynamic Neural Networks,” IEEE Int. Symp. On Intelligent Control (ISIC’97), İstanbul 1997
[13] Konar A. F., Oysal Y., Becerikli Y., Samad T., “Dynamic Fuzzy Networks for Real-Time Applications”, Türkiye Artificial Intelligence & Neural Network Symposium, TAINN'99, Bogaziçi University, March 1999
[14] Lewis F. L., Optimal Estimation, John Wiley and Sons, New York, 1986
[15] Ljung L., System Identification Theory for the User, Prentice-Hall, N. J., USA, 1987
[16] Morris A. J., “On artificial neural networks in process engineering.’’ IEE Proc. Pt. D. Control Theory and Applications, 1992.
[17] Oysal Y., Fero Modelleme ve Optimal Bulanık Kontrol, Doktora Tezi, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Mart 2002
[18] Ray. W. H., “New Approaches to the Dynamics of Nonlinear Systems with Implications for Process and Control Design.” Chemical Process Control, Edgar Seaburg Editions, 2:119, 1981
[19] Shanno D. F., “ Conditioning of Quasi-Newton for function minimization.”, Math. Comp.,24:647-656, 1970.
[20] Wang L. X., A Course in Fuzzy Systems and Control, Prentice-Hall International, Upper Saddle River, New Jersey, 1997.
[21] Zadeh L. A.,. and Desoer C. A., Linear System Theory, McGraw-Hill Book Company, NewYork, 1963.

Thank you for copying data from http://www.arastirmax.com